Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 728

Search results for: frequent itemsets

728 Using Closed Frequent Itemsets for Hierarchical Document Clustering

Authors: Cheng-Jhe Lee, Chiun-Chieh Hsu

Abstract:

Due to the rapid development of the Internet and the increased availability of digital documents, the excessive information on the Internet has led to information overflow problem. In order to solve these problems for effective information retrieval, document clustering in text mining becomes a popular research topic. Clustering is the unsupervised classification of data items into groups without the need of training data. Many conventional document clustering methods perform inefficiently for large document collections because they were originally designed for relational database. Therefore they are impractical in real-world document clustering and require special handling for high dimensionality and high volume. We propose the FIHC (Frequent Itemset-based Hierarchical Clustering) method, which is a hierarchical clustering method developed for document clustering, where the intuition of FIHC is that there exist some common words for each cluster. FIHC uses such words to cluster documents and builds hierarchical topic tree. In this paper, we combine FIHC algorithm with ontology to solve the semantic problem and mine the meaning behind the words in documents. Furthermore, we use the closed frequent itemsets instead of only use frequent itemsets, which increases efficiency and scalability. The experimental results show that our method is more accurate than those of well-known document clustering algorithms.

Keywords: FIHC, documents clustering, ontology, closed frequent itemset

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727 An Enhanced MEIT Approach for Itemset Mining Using Levelwise Pruning

Authors: Tanvi P. Patel, Warish D. Patel

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Association rule mining forms the core of data mining and it is termed as one of the well-known methodologies of data mining. Objectives of mining is to find interesting correlations, frequent patterns, associations or casual structures among sets of items in the transaction databases or other data repositories. Hence, association rule mining is imperative to mine patterns and then generate rules from these obtained patterns. For efficient targeted query processing, finding frequent patterns and itemset mining, there is an efficient way to generate an itemset tree structure named Memory Efficient Itemset Tree. Memory efficient IT is efficient for storing itemsets, but takes more time as compare to traditional IT. The proposed strategy generates maximal frequent itemsets from memory efficient itemset tree by using levelwise pruning. For that firstly pre-pruning of items based on minimum support count is carried out followed by itemset tree reconstruction. By having maximal frequent itemsets, less number of patterns are generated as well as tree size is also reduced as compared to MEIT. Therefore, an enhanced approach of memory efficient IT proposed here, helps to optimize main memory overhead as well as reduce processing time.

Keywords: association rule mining, itemset mining, itemset tree, meit, maximal frequent pattern

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726 On an Approach for Rule Generation in Association Rule Mining

Authors: B. Chandra

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In Association Rule Mining, much attention has been paid for developing algorithms for large (frequent/closed/maximal) itemsets but very little attention has been paid to improve the performance of rule generation algorithms. Rule generation is an important part of Association Rule Mining. In this paper, a novel approach named NARG (Association Rule using Antecedent Support) has been proposed for rule generation that uses memory resident data structure named FCET (Frequent Closed Enumeration Tree) to find frequent/closed itemsets. In addition, the computational speed of NARG is enhanced by giving importance to the rules that have lower antecedent support. Comparative performance evaluation of NARG with fast association rule mining algorithm for rule generation has been done on synthetic datasets and real life datasets (taken from UCI Machine Learning Repository). Performance analysis shows that NARG is computationally faster in comparison to the existing algorithms for rule generation.

Keywords: knowledge discovery, association rule mining, antecedent support, rule generation

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725 Frequent Itemset Mining Using Rough-Sets

Authors: Usman Qamar, Younus Javed

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Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and rough-sets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%.

Keywords: rough-sets, classification, feature selection, entropy, outliers, frequent itemset mining

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724 An Optimized Association Rule Mining Algorithm

Authors: Archana Singh, Jyoti Agarwal, Ajay Rana

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Data Mining is an efficient technology to discover patterns in large databases. Association Rule Mining techniques are used to find the correlation between the various item sets in a database, and this co-relation between various item sets are used in decision making and pattern analysis. In recent years, the problem of finding association rules from large datasets has been proposed by many researchers. Various research papers on association rule mining (ARM) are studied and analyzed first to understand the existing algorithms. Apriori algorithm is the basic ARM algorithm, but it requires so many database scans. In DIC algorithm, less amount of database scan is needed but complex data structure lattice is used. The main focus of this paper is to propose a new optimized algorithm (Friendly Algorithm) and compare its performance with the existing algorithms A data set is used to find out frequent itemsets and association rules with the help of existing and proposed (Friendly Algorithm) and it has been observed that the proposed algorithm also finds all the frequent itemsets and essential association rules from databases as compared to existing algorithms in less amount of database scan. In the proposed algorithm, an optimized data structure is used i.e. Graph and Adjacency Matrix.

Keywords: association rules, data mining, dynamic item set counting, FP-growth, friendly algorithm, graph

Procedia PDF Downloads 332
723 Efficient Recommendation System for Frequent and High Utility Itemsets over Incremental Datasets

Authors: J. K. Kavitha, D. Manjula, U. Kanimozhi

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Mining frequent and high utility item sets have gained much significance in the recent years. When the data arrives sporadically, incremental and interactive rule mining and utility mining approaches can be adopted to handle user’s dynamic environmental needs and avoid redundancies, using previous data structures, and mining results. The dependence on recommendation systems has exponentially risen since the advent of search engines. This paper proposes a model for building a recommendation system that suggests frequent and high utility item sets over dynamic datasets for a cluster based location prediction strategy to predict user’s trajectories using the Efficient Incremental Rule Mining (EIRM) algorithm and the Fast Update Utility Pattern Tree (FUUP) algorithm. Through comprehensive evaluations by experiments, this scheme has shown to deliver excellent performance.

Keywords: data sets, recommendation system, utility item sets, frequent item sets mining

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722 Frequent Item Set Mining for Big Data Using MapReduce Framework

Authors: Tamanna Jethava, Rahul Joshi

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Frequent Item sets play an essential role in many data Mining tasks that try to find interesting patterns from the database. Typically it refers to a set of items that frequently appear together in transaction dataset. There are several mining algorithm being used for frequent item set mining, yet most do not scale to the type of data we presented with today, so called “BIG DATA”. Big Data is a collection of large data sets. Our approach is to work on the frequent item set mining over the large dataset with scalable and speedy way. Big Data basically works with Map Reduce along with HDFS is used to find out frequent item sets from Big Data on large cluster. This paper focuses on using pre-processing & mining algorithm as hybrid approach for big data over Hadoop platform.

Keywords: frequent item set mining, big data, Hadoop, MapReduce

Procedia PDF Downloads 259
721 Data Stream Association Rule Mining with Cloud Computing

Authors: B. Suraj Aravind, M. H. M. Krishna Prasad

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There exist emerging applications of data streams that require association rule mining, such as network traffic monitoring, web click streams analysis, sensor data, data from satellites etc. Data streams typically arrive continuously in high speed with huge amount and changing data distribution. This raises new issues that need to be considered when developing association rule mining techniques for stream data. This paper proposes to introduce an improved data stream association rule mining algorithm by eliminating the limitation of resources. For this, the concept of cloud computing is used. Inclusion of this may lead to additional unknown problems which needs further research.

Keywords: data stream, association rule mining, cloud computing, frequent itemsets

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720 Association Rules Mining and NOSQL Oriented Document in Big Data

Authors: Sarra Senhadji, Imene Benzeguimi, Zohra Yagoub

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Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.

Keywords: Apriori, Association rules mining, Big Data, Data Mining, Hadoop, MapReduce, MongoDB, NoSQL

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719 Hybrid Approximate Structural-Semantic Frequent Subgraph Mining

Authors: Montaceur Zaghdoud, Mohamed Moussaoui, Jalel Akaichi

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Frequent subgraph mining refers usually to graph matching and it is widely used in when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structuralsemantic graph matching to discover a set of frequent subgraphs. It uses simultaneously an approximate structural similarity function based on graph edit distance function and a possibilistic vertices similarity function based on affinity function. Both structural and semantic filters contribute together to prune extracted frequent set. Indeed, new hybrid structural-semantic frequent subgraph mining approach searches will be suitable to be applied to several application such as community detection in social networks.

Keywords: approximate graph matching, hybrid frequent subgraph mining, graph mining, possibility theory

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718 Frequent-Flyer Program: The Connection between Commercial Partners and Spin-off

Authors: Changmin Jiang

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In this paper, we build a theoretical model to investigate the relationship between two recent trends in airline frequent-flyer programs (FFPs): the adoption of the “coalition” business model with other commercial partners, and the separation from airlines’ operations. We show that commercial partners benefit from teaming up with FFP, while increasing the number of commercial partners will increase the total profit; it reduces the average profit of the parties involved. Furthermore, we show that the number of commercial partners of an FFP is negatively related with the benefit to keep the FFP in-house.

Keywords: frequent flyer program, coalition, commercial partners, spin-off

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717 Efficient Frequent Itemset Mining Methods over Real-Time Spatial Big Data

Authors: Hamdi Sana, Emna Bouazizi, Sami Faiz

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In recent years, there is a huge increase in the use of spatio-temporal applications where data and queries are continuously moving. As a result, the need to process real-time spatio-temporal data seems clear and real-time stream data management becomes a hot topic. Sliding window model and frequent itemset mining over dynamic data are the most important problems in the context of data mining. Thus, sliding window model for frequent itemset mining is a widely used model for data stream mining due to its emphasis on recent data and its bounded memory requirement. These methods use the traditional transaction-based sliding window model where the window size is based on a fixed number of transactions. Actually, this model supposes that all transactions have a constant rate which is not suited for real-time applications. And the use of this model in such applications endangers their performance. Based on these observations, this paper relaxes the notion of window size and proposes the use of a timestamp-based sliding window model. In our proposed frequent itemset mining algorithm, support conditions are used to differentiate frequents and infrequent patterns. Thereafter, a tree is developed to incrementally maintain the essential information. We evaluate our contribution. The preliminary results are quite promising.

Keywords: real-time spatial big data, frequent itemset, transaction-based sliding window model, timestamp-based sliding window model, weighted frequent patterns, tree, stream query

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716 Discussion about Frequent Adjustment of Urban Master Planning in China: A Case Study of Changshou District, Chongqing City

Authors: Sun Ailu, Zhao Wanmin

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Since the reform and opening, the urbanization process of China has entered a rapid development period. In recent years, the authors participated in some projects of urban master planning in China and found a phenomenon that the rapid urbanization area of China is experiencing frequent adjustment process of urban master planning. This phenomenon is not the natural process of urbanization development. It may be caused by different government roles from different levels. Through the methods of investigation, data comparison and case study, this paper aims to explore the reason why the rapid urbanization area is experiencing frequent adjustment of master planning and give some solution strategies. Firstly, taking Changshou district of Chongqing city as an example, this paper wants to introduce the phenomenon about frequent adjustment process in China. And then, discuss distinct roles in the process between national government, provincial government and local government of China. At last, put forward preliminary solutions strategies for this area in China from the aspects of land use, intergovernmental cooperation and so on.

Keywords: urban master planning, frequent adjustment, urbanization development, problems and strategies, China

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715 Trends and Inequalities in Distance to and Use of Nearest Natural Space in the Context of the 20-Minute Neighbourhood: A 4-Wave National Repeat Crosssectional Study, 2013 to 2019

Authors: Jonathan R Olsen, Natalie Nicholls, Jenna Panter, Hannah Burnett, Michael Tornow, Richard Mitchell

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The 20-minute neighborhood is a policy priority for governments worldwide and a key feature of this policy is providing access to natural space within 800 meters of home. The study aims were to (1) examine the association between distance to nearest natural space and frequent use over time and (2) examine whether frequent use and changes in use were patterned by income and housing tenure over time. Bi-annual Scottish Household Survey data were obtained for 2013 to 2019 (n:42128 aged 16+). Adults were asked the walking distance to their nearest natural space, the frequency of visits to this space and their housing tenure, as well as age, sex and income. We examined the association between distance from home of nearest natural space, housing tenure, and the likelihood of frequent natural space use (visited once a week or more). Two-way interaction terms were further applied to explore variation in the association between tenure and frequent natural space use over time. We found that 87% of respondents lived within 10 minute walk of a natural space, meeting the policy specification for a 20-minute neighbourhood. Greater proximity to natural space was associated with increased use; individuals living a 6 to 10 minute walk and over 10 minute walk were respectively 53% and 78% less likely to report frequent natural space use than those living within a 5 minute walk. Housing tenure was an important predictor of frequent natural space use; private renters and homeowners were more likely to report frequent natural space use than social renters. Our findings provide evidence that proximity to natural space is a strong predictor of frequent use. Our study provides important evidence that time-based access measures alone do not consider deep-rooted socioeconomic variation in use of Natural space. Policy makers should ensure a nuanced lens is applied to operationalising and monitoring the 20-minute neighbourhood to safeguard against exacerbating existing inequalities.

Keywords: natural space, housing, inequalities, 20-minute neighbourhood, urban design

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714 Correction of Frequent English Writing Errors by Using Coded Indirect Corrective Feedback and Error Treatment

Authors: Chaiwat Tantarangsee

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The purposes of this study are: 1) to study the frequent English writing errors of students registering the course: Reading and Writing English for Academic Purposes II, and 2) to find out the results of writing error correction by using coded indirect corrective feedback and writing error treatments. Samples include 28 2nd year English Major students, Faculty of Education, Suan Sunandha Rajabhat University. Tool for experimental study includes the lesson plan of the course; Reading and Writing English for Academic Purposes II, and tool for data collection includes 4 writing tests of short texts. The research findings disclose that frequent English writing errors found in this course comprise 7 types of grammatical errors, namely Fragment sentence, Subject-verb agreement, Wrong form of verb tense, Singular or plural noun endings, Run-ons sentence, Wrong form of verb pattern and Lack of parallel structure. Moreover, it is found that the results of writing error correction by using coded indirect corrective feedback and error treatment reveal the overall reduction of the frequent English writing errors and the increase of students’ achievement in the writing of short texts with the significance at .05.

Keywords: coded indirect corrective feedback, error correction, error treatment, frequent English writing errors

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713 HPPDFIM-HD: Transaction Distortion and Connected Perturbation Approach for Hierarchical Privacy Preserving Distributed Frequent Itemset Mining over Horizontally-Partitioned Dataset

Authors: Fuad Ali Mohammed Al-Yarimi

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Many algorithms have been proposed to provide privacy preserving in data mining. These protocols are based on two main approaches named as: the perturbation approach and the Cryptographic approach. The first one is based on perturbation of the valuable information while the second one uses cryptographic techniques. The perturbation approach is much more efficient with reduced accuracy while the cryptographic approach can provide solutions with perfect accuracy. However, the cryptographic approach is a much slower method and requires considerable computation and communication overhead. In this paper, a new scalable protocol is proposed which combines the advantages of the perturbation and distortion along with cryptographic approach to perform privacy preserving in distributed frequent itemset mining on horizontally distributed data. Both the privacy and performance characteristics of the proposed protocol are studied empirically.

Keywords: anonymity data, data mining, distributed frequent itemset mining, gaussian perturbation, perturbation approach, privacy preserving data mining

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712 Frequent-Pattern Tree Algorithm Application to S&P and Equity Indexes

Authors: E. Younsi, H. Andriamboavonjy, A. David, S. Dokou, B. Lemrabet

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Software and time optimization are very important factors in financial markets, which are competitive fields, and emergence of new computer tools further stresses the challenge. In this context, any improvement of technical indicators which generate a buy or sell signal is a major issue. Thus, many tools have been created to make them more effective. This worry about efficiency has been leading in present paper to seek best (and most innovative) way giving largest improvement in these indicators. The approach consists in attaching a signature to frequent market configurations by application of frequent patterns extraction method which is here most appropriate to optimize investment strategies. The goal of proposed trading algorithm is to find most accurate signatures using back testing procedure applied to technical indicators for improving their performance. The problem is then to determine the signatures which, combined with an indicator, outperform this indicator alone. To do this, the FP-Tree algorithm has been preferred, as it appears to be the most efficient algorithm to perform this task.

Keywords: quantitative analysis, back-testing, computational models, apriori algorithm, pattern recognition, data mining, FP-tree

Procedia PDF Downloads 281
711 Correction of Frequent English Writing Errors by Using Coded Indirect Corrective Feedback and Error Treatment: The Case of Reading and Writing English for Academic Purposes II

Authors: Chaiwat Tantarangsee

Abstract:

The purposes of this study are 1) to study the frequent English writing errors of students registering the course: Reading and Writing English for Academic Purposes II, and 2) to find out the results of writing error correction by using coded indirect corrective feedback and writing error treatments. Samples include 28 2nd year English Major students, Faculty of Education, Suan Sunandha Rajabhat University. Tool for experimental study includes the lesson plan of the course; Reading and Writing English for Academic Purposes II, and tool for data collection includes 4 writing tests of short texts. The research findings disclose that frequent English writing errors found in this course comprise 7 types of grammatical errors, namely Fragment sentence, Subject-verb agreement, Wrong form of verb tense, Singular or plural noun endings, Run-ons sentence, Wrong form of verb pattern and Lack of parallel structure. Moreover, it is found that the results of writing error correction by using coded indirect corrective feedback and error treatment reveal the overall reduction of the frequent English writing errors and the increase of students’ achievement in the writing of short texts with the significance at .05.

Keywords: coded indirect corrective feedback, error correction, error treatment, English writing

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710 Recognizing Customer Preferences Using Review Documents: A Hybrid Text and Data Mining Approach

Authors: Oshin Anand, Atanu Rakshit

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The vast increment in the e-commerce ventures makes this area a prominent research stream. Besides several quantified parameters, the textual content of reviews is a storehouse of many information that can educate companies and help them earn profit. This study is an attempt in this direction. The article attempts to categorize data based on a computed metric that quantifies the influencing capacity of reviews rendering two categories of high and low influential reviews. Further, each of these document is studied to conclude several product feature categories. Each of these categories along with the computed metric is converted to linguistic identifiers and are used in an association mining model. The article makes a novel attempt to combine feature attraction with quantified metric to categorize review text and finally provide frequent patterns that depict customer preferences. Frequent mentions in a highly influential score depict customer likes or preferred features in the product whereas prominent pattern in low influencing reviews highlights what is not important for customers. This is achieved using a hybrid approach of text mining for feature and term extraction, sentiment analysis, multicriteria decision-making technique and association mining model.

Keywords: association mining, customer preference, frequent pattern, online reviews, text mining

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709 Phonological Variation in the Speech of Grade 1 Teachers in Select Public Elementary Schools in the Philippines

Authors: M. Leonora D. Guerrero

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The study attempted to uncover the most and least frequent phonological variation evident in the speech patterns of grade 1 teachers in select public elementary schools in the Philippines. It also determined the lectal description of the participants based on Tayao’s consonant charts for American and Philippine English. Descriptive method was utilized. A total of 24 grade 1 teachers participated in the study. The instrument used was word list. Each column in the word list is represented by words with the target consonant phonemes: labiodental fricatives f/ and /v/ and lingua-alveolar fricative /z/. These phonemes were in the initial, medial, and final positions, respectively. Findings of the study revealed that the most frequent variation happened when the participants read words with /z/ in the final position while the least frequent variation happened when the participants read words with /z/ in the initial position. The study likewise proved that the grade 1 teachers exhibited the segmental features of both the mesolect and basilect. Based on these results, it is suggested that teachers of English in the Philippines must aspire to manifest the features of the mesolect, if not, the acrolect since it is expected of the academicians not to be displaying the phonological features of the acrolects since this variety is only used by the 'uneducated.' This is especially so with grade 1 teachers who are often mimicked by their students who classify their speech as the 'standard.'

Keywords: consonant phonemes, lectal description, Philippine English, phonological variation

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708 Affects Associations Analysis in Emergency Situations

Authors: Joanna Grzybowska, Magdalena Igras, Mariusz Ziółko

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Association rule learning is an approach for discovering interesting relationships in large databases. The analysis of relations, invisible at first glance, is a source of new knowledge which can be subsequently used for prediction. We used this data mining technique (which is an automatic and objective method) to learn about interesting affects associations in a corpus of emergency phone calls. We also made an attempt to match revealed rules with their possible situational context. The corpus was collected and subjectively annotated by two researchers. Each of 3306 recordings contains information on emotion: (1) type (sadness, weariness, anxiety, surprise, stress, anger, frustration, calm, relief, compassion, contentment, amusement, joy) (2) valence (negative, neutral, or positive) (3) intensity (low, typical, alternating, high). Also, additional information, that is a clue to speaker’s emotional state, was annotated: speech rate (slow, normal, fast), characteristic vocabulary (filled pauses, repeated words) and conversation style (normal, chaotic). Exponentially many rules can be extracted from a set of items (an item is a previously annotated single information). To generate the rules in the form of an implication X → Y (where X and Y are frequent k-itemsets) the Apriori algorithm was used - it avoids performing needless computations. Then, two basic measures (Support and Confidence) and several additional symmetric and asymmetric objective measures (e.g. Laplace, Conviction, Interest Factor, Cosine, correlation coefficient) were calculated for each rule. Each applied interestingness measure revealed different rules - we selected some top rules for each measure. Owing to the specificity of the corpus (emergency situations), most of the strong rules contain only negative emotions. There are though strong rules including neutral or even positive emotions. Three examples of the strongest rules are: {sadness} → {anxiety}; {sadness, weariness, stress, frustration} → {anger}; {compassion} → {sadness}. Association rule learning revealed the strongest configurations of affects (as well as configurations of affects with affect-related information) in our emergency phone calls corpus. The acquired knowledge can be used for prediction to fulfill the emotional profile of a new caller. Furthermore, a rule-related possible context analysis may be a clue to the situation a caller is in.

Keywords: data mining, emergency phone calls, emotional profiles, rules

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707 Implementation of Complete Management Practices in Managing the Cocoa Pod Borer

Authors: B. Saripah, A. Alias

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Cocoa Theobroma cacao (Linnaeus) (Malvales: Sterculiaceae) is subjected to be infested by various numbers of insect pests, and Conopomorpha cramerella Snellen (Lepidoptera: Gracillariidae) is the most serious pest of cocoa in Malaysia. The pest was indigenous to the South East Asia. Several control measures have been implemented and the chemicals have been a major approach if not unilateral, in the management of CPB. Despite extensive use of insecticides, CPB continues to cause an unacceptable level of damage; thus, the combination of several control approaches should be sought. The study was commenced for 12 months at three blocks; Block 18C with complete management practices which include insecticide application, pruning, fertilization and frequent harvesting, Block 17C was treated with frequent harvesting at intervals of 7-8 days, and Block 19C was served as control block. The results showed that the mean numbers of CPB eggs were recorded higher in Block 17C compared with Block 18C in all sampling occasions. Block 18C shows the lowest mean number of CPB eggs in both sampling plots, outside and core plots and it was found significantly different (p ≤ 0. 05) compared to the other blocks. The mean number of CPB eggs was fluctuated throughout sampling occasions, the lowest mean number of eggs was recorded in January (17C) and November (18C), while the highest was recorded in April (17C) and December 2012 (18C). Frequent spraying with insecticides at the adjacent block (18C) helps in reducing CPB eggs in the control block (Block 19C), although there was no spraying was implemented Block 19C. In summary, the combination of complete management practices at Block 18C seems to have some effect on the CPB population at Blocks 17 and 19C because all blocks are adjacent to each other.

Keywords: cocoa, theobroma cacao, cocoa pod borer, conopomorpha cramerella

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706 Design of a Pneumonia Ontology for Diagnosis Decision Support System

Authors: Sabrina Azzi, Michal Iglewski, Véronique Nabelsi

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Diagnosis error problem is frequent and one of the most important safety problems today. One of the main objectives of our work is to propose an ontological representation that takes into account the diagnostic criteria in order to improve the diagnostic. We choose pneumonia disease since it is one of the frequent diseases affected by diagnosis errors and have harmful effects on patients. To achieve our aim, we use a semi-automated method to integrate diverse knowledge sources that include publically available pneumonia disease guidelines from international repositories, biomedical ontologies and electronic health records. We follow the principles of the Open Biomedical Ontologies (OBO) Foundry. The resulting ontology covers symptoms and signs, all the types of pneumonia, antecedents, pathogens, and diagnostic testing. The first evaluation results show that most of the terms are covered by the ontology. This work is still in progress and represents a first and major step toward a development of a diagnosis decision support system for pneumonia.

Keywords: Clinical decision support system, Diagnostic errors, Ontology, Pneumonia

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705 Wind Comfort and Safety of People in the Vicinity of Tall Buildings

Authors: Mohan Kotamrazu

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Tall buildings block and divert strong upper level winds to the ground. These high velocity winds many a time create adverse wind effects at ground level which can be uncomfortable and even compromise the safety of pedestrians and people who frequent the spaces in the vicinity of tall buildings. Discomfort can be experienced around the entrances and corners of tall buildings. Activities such as strolling or sitting in a park, waiting for a bus near a tall building can become highly unpleasant. For the elderly unpleasant conditions can also become dangerous leading to accidents and injuries. Today there is a growing concern among architects, planners and urban designers about the wind environment in the vicinity of tall building. Regulating authorities insist on wind tunnel testing of tall buildings in cities such as Wellington, Auckland, Boston, San Francisco, etc. prior to granting permission for their construction The present paper examines the different ways that tall buildings can induce strong winds at pedestrian level and their impact on people who frequent the spaces around tall buildings.

Keywords: tall buildings, wind effects, wind comfort, wind safety

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704 RA-Apriori: An Efficient and Faster MapReduce-Based Algorithm for Frequent Itemset Mining on Apache Flink

Authors: Sanjay Rathee, Arti Kashyap

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Extraction of useful information from large datasets is one of the most important research problems. Association rule mining is one of the best methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent patterns) is most important part of the association rule mining. There exist many algorithms to find frequent patterns but Apriori algorithm always remains a preferred choice due to its ease of implementation and natural tendency to be parallelized. Many single-machine based Apriori variants exist but massive amount of data available these days is above capacity of a single machine. Therefore, to meet the demands of this ever-growing huge data, there is a need of multiple machines based Apriori algorithm. For these types of distributed applications, MapReduce is a popular fault-tolerant framework. Hadoop is one of the best open-source software frameworks with MapReduce approach for distributed storage and distributed processing of huge datasets using clusters built from commodity hardware. However, heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes Hadoop inefficient. A number of MapReduce-based platforms are being developed for parallel computing in recent years. Among them, two platforms, namely, Spark and Flink have attracted a lot of attention because of their inbuilt support to distributed computations. Earlier we proposed a reduced- Apriori algorithm on Spark platform which outperforms parallel Apriori, one because of use of Spark and secondly because of the improvement we proposed in standard Apriori. Therefore, this work is a natural sequel of our work and targets on implementing, testing and benchmarking Apriori and Reduced-Apriori and our new algorithm ReducedAll-Apriori on Apache Flink and compares it with Spark implementation. Flink, a streaming dataflow engine, overcomes disk I/O bottlenecks in MapReduce, providing an ideal platform for distributed Apriori. Flink's pipelining based structure allows starting a next iteration as soon as partial results of earlier iteration are available. Therefore, there is no need to wait for all reducers result to start a next iteration. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of the Apriori and RA-Apriori algorithm on Flink.

Keywords: apriori, apache flink, Mapreduce, spark, Hadoop, R-Apriori, frequent itemset mining

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703 A Comparative Analysis of Green Buildings Rating Systems

Authors: Shadi Motamedighazvini, Roohollah Taherkhani, Mahdi Mahdikhani, Najme Hashempour

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Nowadays, green building rating systems are an inevitable necessity for managing environmental considerations to achieve green buildings. The aim of this paper is to deliver a detailed recognition of what has been the focus of green building policymakers around the world; It is important to conduct this study in a way that can provide a context for researchers who intend to establish or upgrade existing rating systems. In this paper, fifteen rating systems including four worldwide well-known plus eleven local rating systems which have been selected based on the answers to the questionnaires were examined. Their similarities and differences in mandatory and prerequisite clauses, highest and lowest scores for each criterion, the most frequent criteria, and most frequent sub-criteria are determined. The research findings indicated that although the criteria of energy, water, indoor quality (except Homestar), site and materials (except GRIHA) were common core criteria for all rating systems, their sub-criteria were different. This research, as a roadmap, eliminates the lack of a comprehensive reference that encompasses the key criteria of different rating systems. It shows the local systems need to be revised to be more comprehensive and adaptable to their own country’s conditions such as climate.

Keywords: environmental assessment, green buildings, green building criteria, green building rating systems, sustainability, rating tools

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702 Predicting Medical Check-Up Patient Re-Coming Using Sequential Pattern Mining and Association Rules

Authors: Rizka Aisha Rahmi Hariadi, Chao Ou-Yang, Han-Cheng Wang, Rajesri Govindaraju

Abstract:

As the increasing of medical check-up popularity, there are a huge number of medical check-up data stored in database and have not been useful. These data actually can be very useful for future strategic planning if we mine it correctly. In other side, a lot of patients come with unpredictable coming and also limited available facilities make medical check-up service offered by hospital not maximal. To solve that problem, this study used those medical check-up data to predict patient re-coming. Sequential pattern mining (SPM) and association rules method were chosen because these methods are suitable for predicting patient re-coming using sequential data. First, based on patient personal information the data was grouped into … groups then discriminant analysis was done to check significant of the grouping. Second, for each group some frequent patterns were generated using SPM method. Third, based on frequent patterns of each group, pairs of variable can be extracted using association rules to get general pattern of re-coming patient. Last, discussion and conclusion was done to give some implications of the results.

Keywords: patient re-coming, medical check-up, health examination, data mining, sequential pattern mining, association rules, discriminant analysis

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701 The Effects of Collaborative Videogame Play on Flow Experience and Mood

Authors: Eva Nolan, Timothy Mcnichols

Abstract:

Gamers spend over 3 billion hours collectively playing video games a week, which is arguably not nearly enough time to indulge in the many benefits gaming has to offer. Much of the previous research on video gaming is centered on the effects of playing violent video games and the negative impacts they have on the individual. However, there is a dearth of research in the area of non-violent video games, specifically the emotional and cognitive benefits playing non-violent games can offer individuals. Current research in the area of video game play suggests there are many benefits to playing for an individual, such as decreasing symptoms of depression, decreasing stress, increasing positive emotions, inducing relaxation, decreasing anxiety, and particularly improving mood. One suggestion as to why video games may offer such benefits is that they possess ideal characteristics to create and maintain flow experiences, which in turn, is the subjective experience where an individual obtains a heightened and improved state of mind while they are engaged in a task where a balance of challenge and skill is found. Many video games offer a platform for collaborative gameplay, which can enhance the emotional experience of gaming through the feeling of social support and social inclusion. The present study was designed to examine the effects of collaborative gameplay and flow experience on participants’ perceived mood. To investigate this phenomenon, an in-between subjects design involving forty participants were randomly divided into two groups where they engaged in solo or collaborative gameplay. Each group represented an even number of frequent gamers and non-frequent gamers. Each participant played ‘The Lego Movie Videogame’ on the Playstation 4 console. The participant’s levels of flow experience and perceived mood were measured by the Flow State Scale (FSS) and the Positive and Negative Affect Schedule (PANAS). The following research hypotheses were investigated: (i.) participants in the collaborative gameplay condition will experience higher levels of flow experience and higher levels of mood than those in the solo gameplay condition; (ii.) participants who are frequent gamers will experience higher levels of flow experience and higher levels of mood than non-frequent gamers; and (iii.) there will be a significant positive relationship between flow experience and mood. If the estimated findings are supported, this suggests that engaging in collaborative gameplay can be beneficial for an individual’s mood and that experiencing a state of flow can also enhance an individual’s mood. Hence, collaborative gaming can be beneficial to promote positive emotions (higher levels of mood) through engaging an individual’s flow state.

Keywords: collaborative gameplay, flow experience, mood, games, positive emotions

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700 A Study of the Use of Arguments in Nominalizations as Instanciations of Grammatical Metaphors Finished in -TION in Academic Texts of Native Speakers

Authors: Giovana Perini-Loureiro

Abstract:

The purpose of this research was to identify whether the nominalizations terminating in -TION in the academic discourse of native English speakers contain the arguments required by their input verbs. In the perspective of functional linguistics, ideational metaphors, with nominalization as their most pervasive realization, are lexically dense, and therefore frequent in formal texts. Ideational metaphors allow the academic genre to instantiate objectification, de-personalization, and the ability to construct a chain of arguments. The valence of those nouns present in nominalizations tends to maintain the same elements of the valence from its original verbs, but these arguments are not always expressed. The initial hypothesis was that these arguments would also be present alongside the nominalizations, through anaphora or cataphora. In this study, a qualitative analysis of the occurrences of the five more frequent nominalized terminations in -TION in academic texts was accomplished, and thus a verification of the occurrences of the arguments required by the original verbs. The assembling of the concordance lines was done through COCA (Corpus of Contemporary American English). After identifying the five most frequent nominalizations (attention, action, participation, instruction, intervention), the concordance lines were selected at random to be analyzed, assuring the representativeness and reliability of the sample. It was possible to verify, in all the analyzed instances, the presence of arguments. In most instances, the arguments were not expressed, but recoverable, either in the context or in the shared knowledge among the interactants. It was concluded that the realizations of the arguments which were not expressed alongside the nominalizations are part of a continuum, starting from the immediate context with anaphora and cataphora; up to a knowledge shared outside the text, such as specific area knowledge. The study also has implications for the teaching of academic writing, especially with regards to the impact of nominalizations on the thematic and informational flow of the text. Grammatical metaphors are essential to academic writing, hence acknowledging the occurrence of its arguments is paramount to achieve linguistic awareness and the writing prestige required by the academy.

Keywords: corpus, functional linguistics, grammatical metaphors, nominalizations, academic English

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699 Biomarkers in a Post-Stroke Population: Allied to Health Care in Brazil

Authors: Michael Ricardo Lang, AdriéLle Costa, Ivana Iesbik, Karine Haag, Leonardo Trindade Buffara, Oscar Reimann Junior, Chelin Auswaldt Steclan

Abstract:

Stroke affects not only the individual, but has significant impacts on the social and family context. Therefore, it is necessary to know the peculiarities of each region, in order to contribute to regional public health policies effectively. Thus, the present study discusses biomarkers in a post-stroke population, admitted to a stroke unit (U-stroke) of reference in the southern region of Brazil. Biomarkers were analyzed, such as age, length of stay, mortality rate, survival time, risk factors and family history of stroke in patients after ischemic stroke. In this studied population, comparing men and women, it was identified that men were more affected than women, and the average age of women affected was higher, as they also had the highest mortality rate and the shortest hospital stay. The risk factors identified here were according to the global scenario; with SAH being the most frequent and those associated with sedentary lifestyle in women the most frequent (dyspilipidemia, heart disease and obesity). In view of this, the importance of studies that characterize populations regionally is evident, strengthening the strategic planning of policies in favor of health care.

Keywords: biomarkers, sex, stroke, stroke unit, population

Procedia PDF Downloads 148