Search results for: frequent and non-frequent items

Authors: R. Vishnu Priya, A. Vadivel

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Sequential pattern mining is a challenging task in data mining area with large applications. One among those applications is mining patterns from weblog. Recent times, weblog is highly dynamic and some of them may become absolute over time. In addition, users may frequently change the threshold value during the data mining process until acquiring required output or mining interesting rules. Some of the recently proposed algorithms for mining weblog, build the tree with two scans and always consume large time and space. In this paper, we build Revised PLWAP with Non-frequent Items (RePLNI-tree) with single scan for all items. While mining sequential patterns, the links related to the nonfrequent items are not considered. Hence, it is not required to delete or maintain the information of nodes while revising the tree for mining updated transactions. The algorithm supports both incremental and interactive mining. It is not required to re-compute the patterns each time, while weblog is updated or minimum support changed. The performance of the proposed tree is better, even the size of incremental database is more than 50% of existing one. For evaluation purpose, we have used the benchmark weblog dataset and found that the performance of proposed tree is encouraging compared to some of the recently proposed approaches.

Keywords: Sequential pattern mining, weblog, frequent and non-frequent items, incremental and interactive mining.

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Authors: Nittaya Kerdprasop, Kittisak Kerdprasop

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Frequent patterns are patterns such as sets of features or items that appear in data frequently. Finding such frequent patterns has become an important data mining task because it reveals associations, correlations, and many other interesting relationships hidden in a dataset. Most of the proposed frequent pattern mining algorithms have been implemented with imperative programming languages such as C, Cµ, Java. The imperative paradigm is significantly inefficient when itemset is large and the frequent pattern is long. We suggest a high-level declarative style of programming using a functional language. Our supposition is that the problem of frequent pattern discovery can be efficiently and concisely implemented via a functional paradigm since pattern matching is a fundamental feature supported by most functional languages. Our frequent pattern mining implementation using the Haskell language confirms our hypothesis about conciseness of the program. The performance studies on speed and memory usage support our intuition on efficiency of functional language.

Keywords: Association, frequent pattern mining, functionalprogramming, pattern matching.

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Authors: Kittisak Kerdprasop, Nittaya Kerdprasop

Abstract:

The problem of frequent pattern discovery is defined as the process of searching for patterns such as sets of features or items that appear in data frequently. Finding such frequent patterns has become an important data mining task because it reveals associations, correlations, and many other interesting relationships hidden in a database. Most of the proposed frequent pattern mining algorithms have been implemented with imperative programming languages. Such paradigm is inefficient when set of patterns is large and the frequent pattern is long. We suggest a high-level declarative style of programming apply to the problem of frequent pattern discovery. We consider two languages: Haskell and Prolog. Our intuitive idea is that the problem of finding frequent patterns should be efficiently and concisely implemented via a declarative paradigm since pattern matching is a fundamental feature supported by most functional languages and Prolog. Our frequent pattern mining implementation using the Haskell and Prolog languages confirms our hypothesis about conciseness of the program. The comparative performance studies on line-of-code, speed and memory usage of declarative versus imperative programming have been reported in the paper.

Keywords: Frequent pattern mining, functional programming, pattern matching, logic programming.

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Authors: Usman Qamar, Younus Javed

Abstract:

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 roughsets 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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Authors: M. Jamali, F. Taghiyareh

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The problem of frequent itemset mining is considered in this paper. One new technique proposed to generate frequent patterns in large databases without time-consuming candidate generation. This technique is based on focusing on transaction instead of concentrating on itemset. This algorithm based on take intersection between one transaction and others transaction and the maximum shared items between transactions computed instead of creating itemset and computing their frequency. With applying real life transactions and some consumption is taken from real life data, the significant efficiency acquire from databases in generation association rules mining.

Keywords: Association rules, data mining, frequent patterns, shared itemset.

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Authors: Moez Ben Hadj Hamida, Yahya SlimaniI

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In this paper, we propose an adaptation of the Patricia-Tree for sparse datasets to generate non redundant rule associations. Using this adaptation, we can generate frequent closed itemsets that are more compact than frequent itemsets used in Apriori approach. This adaptation has been experimented on a set of datasets benchmarks.

Keywords: Datamining, Frequent itemsets, Frequent closeditemsets, Sparse datasets.

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Authors: Mojahid F. Saeed Osman

Abstract:

Repairable items inventory system is a management tool used to incorporate all information concerning inventory levels and movements for repaired and new items. This paper presents development of an effective simulation model for managing the inventory of repairable items for a production system where production lines send their faulty items to a repair shop considering the stochastic failure behavior and repair times. The developed model imitates the process of handling the on-hand inventory of repaired items and the replenishment of the inventory of new items using Economic Order Quantity and Reorder Point ordering policy in a flexible and risk-free environment. We demonstrate the appropriateness and effectiveness of the proposed simulation model using an illustrative case problem. The developed simulation model can be used as a reliable tool for estimating a healthy on-hand inventory of new and repaired items, backordered items, and downtime due to unavailability of repaired items, and validating and examining Economic Order Quantity and Reorder Point ordering policy, which would further be compared with other ordering strategies as future work.

Keywords: Inventory system, repairable items, simulation, maintenance, economic order quantity, reorder point.

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Authors: Kittisak Kerdprasop, Nittaya Kerdprasop

Abstract:

Frequent pattern discovery over data stream is a hard problem because a continuously generated nature of stream does not allow a revisit on each data element. Furthermore, pattern discovery process must be fast to produce timely results. Based on these requirements, we propose an approximate approach to tackle the problem of discovering frequent patterns over continuous stream. Our approximation algorithm is intended to be applied to process a stream prior to the pattern discovery process. The results of approximate frequent pattern discovery have been reported in the paper.

Keywords: Frequent pattern discovery, Approximate algorithm, Data stream analysis.

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Authors: R. AliMohammadzadeh, M. Haghir Chehreghani, A. Zarnani, M. Rahgozar

Abstract:

Mining frequent tree patterns have many useful applications in XML mining, bioinformatics, network routing, etc. Most of the frequent subtree mining algorithms (i.e. FREQT, TreeMiner and CMTreeMiner) use anti-monotone property in the phase of candidate subtree generation. However, none of these algorithms have verified the correctness of this property in tree structured data. In this research it is shown that anti-monotonicity does not generally hold, when using weighed support in tree pattern discovery. As a result, tree mining algorithms that are based on this property would probably miss some of the valid frequent subtree patterns in a collection of trees. In this paper, we investigate the correctness of anti-monotone property for the problem of weighted frequent subtree mining. In addition we propose W3-Miner, a new algorithm for full extraction of frequent subtrees. The experimental results confirm that W3-Miner finds some frequent subtrees that the previously proposed algorithms are not able to discover.

Keywords: Semi-Structured Data Mining, Anti-Monotone Property, Trees.

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Authors: Zalizah Awang Long, Abdul Razak Hamdan, Azuraliza Abu Bakar

Abstract:

Public health surveillance system focuses on outbreak detection and data sources used. Variation or aberration in the frequency distribution of health data, compared to historical data is often used to detect outbreaks. It is important that new techniques be developed to improve the detection rate, thereby reducing wastage of resources in public health. Thus, the objective is to developed technique by applying frequent mining and outlier mining techniques in outbreak detection. 14 datasets from the UCI were tested on the proposed technique. The performance of the effectiveness for each technique was measured by t-test. The overall performance shows that DTK can be used to detect outlier within frequent dataset. In conclusion the outbreak detection technique using anomaly-based on frequent-outlier technique can be used to identify the outlier within frequent dataset.

Keywords: Outlier detection, frequent-outlier, outbreak, anomaly, surveillance, public health

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Authors: Chien-Hua Wang, Chin-Tzong Pang

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In data mining, the association rules are used to search for the relations of items of the transactions database. Following the data is collected and stored, it can find rules of value through association rules, and assist manager to proceed marketing strategy and plan market framework. In this paper, we attempt fuzzy partition methods and decide membership function of quantitative values of each transaction item. Also, by managers we can reflect the importance of items as linguistic terms, which are transformed as fuzzy sets of weights. Next, fuzzy weighted frequent pattern growth (FWFP-Growth) is used to complete the process of data mining. The method above is expected to improve Apriori algorithm for its better efficiency of the whole association rules. An example is given to clearly illustrate the proposed approach.

Keywords: Association Rule, Fuzzy Partition Methods, FWFP-Growth, Apiroir algorithm

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Authors: Chien-Hua Wang, Wei-Hsuan Lee, Chin-Tzong Pang

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In data mining, the association rules are used to find for the associations between the different items of the transactions database. As the data collected and stored, rules of value can be found through association rules, which can be applied to help managers execute marketing strategies and establish sound market frameworks. This paper aims to use Fuzzy Frequent Pattern growth (FFP-growth) to derive from fuzzy association rules. At first, we apply fuzzy partition methods and decide a membership function of quantitative value for each transaction item. Next, we implement FFP-growth to deal with the process of data mining. In addition, in order to understand the impact of Apriori algorithm and FFP-growth algorithm on the execution time and the number of generated association rules, the experiment will be performed by using different sizes of databases and thresholds. Lastly, the experiment results show FFPgrowth algorithm is more efficient than other existing methods.

Keywords: Data mining, association rule, fuzzy frequent patterngrowth.

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Authors: Vaibhav Kant Singh, Vijay Shah, Yogendra Kumar Jain, Anupam Shukla, A.S. Thoke, Vinay KumarSingh, Chhaya Dule, Vivek Parganiha

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Data mining, which is the exploration of knowledge from the large set of data, generated as a result of the various data processing activities. Frequent Pattern Mining is a very important task in data mining. The previous approaches applied to generate frequent set generally adopt candidate generation and pruning techniques for the satisfaction of the desired objective. This paper shows how the different approaches achieve the objective of frequent mining along with the complexities required to perform the job. This paper will also look for hardware approach of cache coherence to improve efficiency of the above process. The process of data mining is helpful in generation of support systems that can help in Management, Bioinformatics, Biotechnology, Medical Science, Statistics, Mathematics, Banking, Networking and other Computer related applications. This paper proposes the use of both upward and downward closure property for the extraction of frequent item sets which reduces the total number of scans required for the generation of Candidate Sets.

Keywords: Data Mining, Candidate Sets, Frequent Item set, Pruning.

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Authors: Renáta Iváncsy, István Vajk

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The various types of frequent pattern discovery problem, namely, the frequent itemset, sequence and graph mining problems are solved in different ways which are, however, in certain aspects similar. The main approach of discovering such patterns can be classified into two main classes, namely, in the class of the levelwise methods and in that of the database projection-based methods. The level-wise algorithms use in general clever indexing structures for discovering the patterns. In this paper a new approach is proposed for discovering frequent sequences and tree-like patterns efficiently that is based on the level-wise issue. Because the level-wise algorithms spend a lot of time for the subpattern testing problem, the new approach introduces the idea of using automaton theory to solve this problem.

Keywords: Frequent pattern discovery, graph mining, pushdownautomaton, sequence mining, state machine, tree mining.

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Authors: Saher Ali Al-Sabbah, See Ching Mey, Ong Saw Lan

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This article addresses the procedures to validate the Arabic version of Multiple Intelligence Development Assessment Scale (MIDAS). The content validity was examined based on the experts- judgments on the MIDAS-s items in the Arabic version. The content of eleven items in the Arabic version of MIDAS was modified to match the Arabic context. Then a translation from original English version of MIDAS into Arabic language was performed. The reliability of the Arabic MIDAS was calculated based on test and retest method and found to be 0.85 for the overall MIDAS and for the different subscales ranging between 0.78 - 0.87. The examination of construct validity for the overall Arabic MIDAS and its subscales was established by using Winsteps program version 6 based on Rasch model in order to fit the items into the Arabic context. The findings indicated that, the eight subscales in Arabic version of MIDAS scale have a unidimensionality, and the total number of kept items in the overall scale is 108 items.

Keywords: Rasch-Model, validation, multiple intelligence, and MIDAS scale.

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Authors: Shigeaki Sakurai, Ryohei Orihara

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This paper proposes a new method for analyzing textual data. The method deals with items of textual data, where each item is described based on various viewpoints. The method acquires 2- class classification models of the viewpoints by applying an inductive learning method to items with multiple viewpoints. The method infers whether the viewpoints are assigned to the new items or not by using the models. The method extracts expressions from the new items classified into the viewpoints and extracts characteristic expressions corresponding to the viewpoints by comparing the frequency of expressions among the viewpoints. This paper also applies the method to questionnaire data given by guests at a hotel and verifies its effect through numerical experiments.

Keywords: Text mining, Multiple viewpoints, Differential analysis, Questionnaire data

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Authors: Seyed Mohammad Nasir Mousavi, Pasha Hejazi, Maryam Ebrahimian Dehkordi

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In order to study the Mutual effect of genotype × environment for the percent of oil index in sunflower items, an experiment was accomplished form complete random block designs in four iteration and was four diverse researching station comprising Esfahan, Birjand, Sari, and Karaj. Complex variance analysis showed that there is an important diversity between the items under investigation. The results relevant the coefficient variation of items Azargol and Vidoc has respectively allocated the minimum coefficient of variations. According to the results extrapolated from Shokla stability variance, the Items Brocar, Allison and Fabiola, are among the stable genotypes for oil percent respectively. In the biplot GGE, the location under investigations divided in two superenvironments, first one comprised of locations naming Esfahan, Karaj, and Birjand, and second one were such a location as Sari. By this point of view, in the first super-environment, the Item Fabiola and in the second Almanzor item was among the best items and crops.

Keywords: Sunflower, Stability, GGE biplot, Super- Environment.

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Authors: F. Rashno Seydari, M. Nikafrooz

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This paper intends to study needs analysis of hearing-impaired students’ teachers in elementary schools all over Iran. The subjects of this study were 275 teachers who were teaching hearing-impaired students in elementary schools. The participants were selected by a quota sampling method. To collect the data, questionnaires of training needs consisting of 41 knowledge items and 31 performance items were used. The collected data were analyzed by using SPSS software in the form of descriptive analyses (frequency and mean) and inferential analyses (one sample t-test, paired t-test, independent t-test, and Pearson correlation coefficient). The findings of the study indicated that teachers generally have considerable needs in knowledge and performance domains. In 32 items out of the total 41 knowledge domain items and in the 27 items out of the total 31 performance domain items, the teachers had considerable needs. From the quantitative point of view, the needs of the performance domain were more than those of the knowledge domain, so they have to be considered as the first priority in training these teachers. There was no difference between the level of the needs of male and female teachers. There was a significant difference between the knowledge and performance domain needs and the teachers’ teaching experience, 0.354 and 0.322 respectively. The teachers who had been trained in working with hearing-impaired students expressed more training needs (both knowledge and performance).

Keywords: Needs analysis, hearing impaired students, hearing impaired students’ teachers, knowledge domain, performance domain.

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Authors: Xiantong Li, Jianzhong Li

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Graph has become increasingly important in modeling complicated structures and schemaless data such as proteins, chemical compounds, and XML documents. Given a graph query, it is desirable to retrieve graphs quickly from a large database via graph-based indices. Different from the existing methods, our approach, called VFM (Vertex to Frequent Feature Mapping), makes use of vertices and decision features as the basic indexing feature. VFM constructs two mappings between vertices and frequent features to answer graph queries. The VFM approach not only provides an elegant solution to the graph indexing problem, but also demonstrates how database indexing and query processing can benefit from data mining, especially frequent pattern mining. The results show that the proposed method not only avoids the enumeration method of getting subgraphs of query graph, but also effectively reduces the subgraph isomorphism tests between the query graph and graphs in candidate answer set in verification stage.

Keywords: Decision Feature, Frequent Feature, Graph Dataset, Graph Query

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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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Authors: Marcus C. Garcia, Giselle C. Guimarães, Luciana H. Yamane, Renato R. Siman

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The environmental indicators and the classification of beach waste are essential tools to diagnose the current situation and to indicate ways to improve the quality of this environment. The purpose of this paper was to perform a quali-quantitative analysis of the beach waste on the Curva da Jurema Beach (Espírito Santo - Brazil). Three transects were used with equidistant positioning over the total length of the beach for the solid waste collection. Solid wastes were later classified according to their use and primary raw material from the low and high summer season. During the low season, average values of 7.10 items.m^-1, 18.22 g.m^-1 and 0.91 g.m^-2 were found for the whole beach, and transect 3 contributed the most waste, with the total sum of items equal to 999 (49%), a total mass of 5.62 kg and a total volume of 21.31 L. During the high summer season, average values of 8.22 items.m^-1, 54.40 g.m^-1 and 2.72 g.m^-2 were found, with transect 2 contributing the most to the total sum with 1,212 items (53%), a total mass of 10.76 kg and a total volume of 51.99 L. Of the total collected, plastic materials represented 51.4% of the total number of items, 35.9% of the total mass and 68% of the total volume. The implementation of reactive and proactive measures is necessary so that the management of the solid wastes on Curva da Jurema Beach is in accordance with principles of sustainability.

Keywords: Beach solid waste, environmental indicators, quali-quantitative analysis, waste management.

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Authors: Chua Bee Seok, Getrude Cosmas, Jasmine Adela Mutang, Shazia Iqbal Hashmi

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The aim of this study was to determine the factor structure and psychometric properties (i.e., reliability and convergent validity) of the Employee Trust Scale, a newly created instrument by the researchers. The Employee Trust Scale initially contained 82 items to measure employees’ trust toward their supervisors. A sample of 818 (343 females, 449 males) employees were selected randomly from public and private organization sectors in Kota Kinabalu, Sabah, Malaysia. Their ages ranged from 19 to 67 years old with a mean of 34.55 years old. Their average tenure with their current employer was 11.2 years (s.d. = 7.5 years). The respondents were asked to complete the Employee Trust Scale, as well as a managerial trust questionnaire from Mishra. The exploratory factor analysis on employees’ trust toward their supervisor’s extracted three factors, labeled ‘trustworthiness’ (32 items), ‘position status’ (11 items) and ‘relationship’ (6 items) which accounted for 62.49% of the total variance. Trustworthiness factors were re-categorized into three sub factors: competency (11 items), benevolence (8 items) and integrity (13 items). All factors and sub factors of the scales demonstrated clear reliability with internal consistency of Cronbach’s Alpha above .85. The convergent validity of the Scale was supported by an expected pattern of correlations (positive and significant correlation) between the score of all factors and sub factors of the scale and the score on the managerial trust questionnaire, which measured the same construct. The convergent validity of Employee Trust Scale was further supported by the significant and positive inter-correlation between the factors and sub factors of the scale. The results suggest that the Employee Trust Scale is a reliable and valid measure. However, further studies need to be carried out in other groups of sample as to further validate the Scale.

Keywords: Employees trust scale, position status, psychometric properties, relationship, trustworthiness.

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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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Authors: Wan Azuati Wan Omar, Selva Rani John Jasudass, Siti Rohaiza Md Saad

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Background: The objectives of this study were to assess patient’s knowledge of appropriate sublingual glyceryl trinitrate (GTN) use as well as to investigate how patients commonly store and carry their sublingual GTN tablets. Methodology: This was a cross-sectional survey, using a validated researcher-administered questionnaire. The study involved cardiac patients receiving sublingual GTN attending the outpatient and inpatient departments of Taiping Hospital, a non-academic public care hospital. The minimum calculated sample size was 92, but 100 patients were conveniently sampled. Respondents were interviewed on 3 areas, including demographic data, knowledge and use of sublingual GTN. Eight items were used to calculate each subject’s knowledge score and six items were used to calculate use score. Results: Of the 96 patients who consented to participate, majority (96.9%) were well aware of the indication of sublingual GTN. With regards to the mechanism of action of sublingual GTN, 73 (76%) patients did not know how the medication works. Majority of the patients (66.7%) knew about the proper storage of the tablet. In relation to the maximum number of sublingual GTN tablets that can be taken during each angina episode, 36.5% did not know that up to 3 tablets of sublingual GTN can be taken during each episode of angina. Fifty four (56.2%) patients were not aware that they need to replace sublingual GTN every 8 weeks after receiving the tablets. Majority (69.8%) of the patients demonstrated lack of knowledge with regards to the use of sublingual GTN as prevention of chest pain. Conclusion: Overall, patients’ knowledge regarding the self-administration of sublingual GTN is still inadequate. The findings support the need for more frequent reinforcement of patient education, especially in the areas of preventive use, storage and drug stability.

Keywords: Glyceryl trinitrate, knowledge, adherence.

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Authors: Nicole Scott, Emily Dwyer, Cara Patrissy, Samantha Bonventre, Lina Begdache

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Across North America, the use and abuse of Attention Deficit Hyperactivity Disorder (ADHD) medication, cannabis, nicotine, and other psychoactive substances across college campuses have become an increasingly prevalent problem. Students frequently use these substances to aid their studying or deal with their mental health issues. However, it is still unknown what psychoactive substances are likely to be abused when college students illicitly use ADHD medication. In addition, it is not clear which psychoactive substance is associated with mental distress. Thus, the purpose of this study is to fill these gaps by assessing the use of different psychoactive substances when illicit ADHD medication is used; and how this association relates to mental stress. A total of 702 undergraduate students from different college campuses in the US completed an anonymous survey distributed online. Data were self-reported on demographics, the use of ADHD medications, cannabis, nicotine, other psychoactive drugs, and mental distress, and feelings and opinions on the use of illicit study drugs were all included in the survey. Mental distress was assessed using the Kessler Psychological Distress 6 Scale. Data were analyzed in SPSS, Version 25.0, using Pearson’s Correlation Coefficient. Our results show use of ADHD medication, cannabis use (non-frequent and very frequent), and nicotine use (non-frequent and very frequent); there were both statistically significant positive and negative correlations to specific psychoactive substances and their corresponding frequencies. Along the same lines, ADHD medication, cannabis use (non-frequent and very frequent), and nicotine use (non-frequent and very frequent) had statistically significant positive and negative correlations to specific mental distress experiences. As these findings are combined, a vicious loop can initiate a cycle where individuals who abuse psychoactive substances may or may not be inclined to use other psychoactive substances. This may later inhibit brain functions in those main areas of the brain stem, amygdala, and prefrontal cortex where this vicious cycle may or may not impact their mental distress. Addressing the impact of study drug abuse and its potential to be associated with further substance abuse may provide an educational framework and support proactive approaches to promote awareness among college students.

Keywords: Stimulant, depressant, nicotine, ADHD medication, psychoactive substances, mental health, illicit, ecstasy, adrenochrome.

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Authors: P. S. Hiremath, Siddu P. Algur

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With the explosive growth of information sources available on the World Wide Web, it has become increasingly difficult to identify the relevant pieces of information, since web pages are often cluttered with irrelevant content like advertisements, navigation-panels, copyright notices etc., surrounding the main content of the web page. Hence, tools for the mining of data regions, data records and data items need to be developed in order to provide value-added services. Currently available automatic techniques to mine data regions from web pages are still unsatisfactory because of their poor performance and tag-dependence. In this paper a novel method to extract data items from the web pages automatically is proposed. It comprises of two steps: (1) Identification and Extraction of the data regions based on visual clues information. (2) Identification of data records and extraction of data items from a data region. For step1, a novel and more effective method is proposed based on visual clues, which finds the data regions formed by all types of tags using visual clues. For step2 a more effective method namely, Extraction of Data Items from web Pages (EDIP), is adopted to mine data items. The EDIP technique is a list-based approach in which the list is a linear data structure. The proposed technique is able to mine the non-contiguous data records and can correctly identify data regions, irrespective of the type of tag in which it is bound. Our experimental results show that the proposed technique performs better than the existing techniques.

Keywords: Web data records, web data regions, web mining.

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Authors: Ian A. McCulloh, Madelaine Zinser, Jesse Patsolic, Michael Ramos

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Recommender systems are a common artificial intelligence (AI) application. For any given input, a search system will return a rank-ordered list of similar items. As users review returned items, they must decide when to halt the search and either revise search terms or conclude their requirement is novel with no similar items in the database. We present a statistically designed experiment that investigates the impact of similarity ratings on human judgement to conclude a search item is novel and halt the search. In the study, 450 participants were recruited from Amazon Mechanical Turk to render judgement across 12 decision tasks. We find the inclusion of ratings increases the human perception that items are novel. Percent similarity increases novelty discernment when compared with star-rated similarity or the absence of a rating. Ratings reduce the time to decide and improve decision confidence. This suggests that the inclusion of similarity ratings can aid human decision-makers in knowledge search tasks.

Keywords: Ratings, rankings, crowdsourcing, empirical studies, user studies, similarity measures, human-centered computing, novelty in information retrieval.

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Authors: Sara Ansari, Mohammad Hadi Sadreddini

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The increasing importance of data stream arising in a wide range of advanced applications has led to the extensive study of mining frequent patterns. Mining data streams poses many new challenges amongst which are the one-scan nature, the unbounded memory requirement and the high arrival rate of data streams. In this paper, we propose a new approach for mining itemsets on data stream. Our approach SFIDS has been developed based on FIDS algorithm. The main attempts were to keep some advantages of the previous approach and resolve some of its drawbacks, and consequently to improve run time and memory consumption. Our approach has the following advantages: using a data structure similar to lattice for keeping frequent itemsets, separating regions from each other with deleting common nodes that results in a decrease in search space, memory consumption and run time; and Finally, considering CPU constraint, with increasing arrival rate of data that result in overloading system, SFIDS automatically detect this situation and discard some of unprocessing data. We guarantee that error of results is bounded to user pre-specified threshold, based on a probability technique. Final results show that SFIDS algorithm could attain about 50% run time improvement than FIDS approach.

Keywords: Data stream, frequent itemset, stream mining.

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Authors: Cheng-Lung Huang, Han-Yu Chien, Michael Conyette

Abstract:

Social bookmarking is an environment in which the user gradually changes interests over time so that the tag data associated with the current temporal period is usually more important than tag data temporally far from the current period. This implies that in the social tagging system, the newly tagged items by the user are more relevant than older items. This study proposes a novel recommender system that considers the users- recent tag preferences. The proposed system includes the following stages: grouping similar users into clusters using an E-M clustering algorithm, finding similar resources based on the user-s bookmarks, and recommending the top-N items to the target user. The study examines the system-s information retrieval performance using a dataset from del.icio.us, which is a famous social bookmarking web site. Experimental results show that the proposed system is better and more effective than traditional approaches.

Keywords: Recommender systems, Social bookmarking, Tag

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Authors: N. I. Bazid, I. N. Umar

Abstract:

Due to the advancement of Internet technology, online learning is widely used in higher education institutions. Online learning offers several means of communication, including online forum. Through online forum, students and instructors are able to discuss and share their knowledge and expertise without having a need to attend the face-to-face, ordinary classroom session. The purposes of this study are to analyze the students’ levels of participation and critical thinking, types of action and factors influencing their participation in online forum. A total of 41 postgraduate students undertaking a course in educational technology from a public university in Malaysia were involved in this study. In this course, the students participated in a weekly online forum as part of the course requirement. Based on the log data file extracted from the online forum, the students’ type of actions (view, add, update, delete posts) and their levels of participation (passive, moderate or active) were identified. In addition, the messages posted in the forum were analyzed to gauge their level of critical thinking. Meanwhile, the factors that might influence their online forum participation were measured using a 24-items questionnaire. Based on the log data, a total of 105 posts were sent by the participants. In addition, the findings show that (i) majority of the students are moderate participants, with an average of two to three posts per person, (ii) viewing posts are the most frequent type of action (85.1%), and followed by adding post (9.7%). Furthermore, based on the posts they made, the most frequent type of critical thinking observed was justification (50 input or 19.0%), followed by linking ideas and interpretation (47 input or 18%), and novelty (38 input or 14.4%). The findings indicate that online forum allows for social interaction and can be used to measure the students’ critical thinking skills. In order to achieve this, monitoring students’ activities in the online forum is recommended.

Keywords: Critical thinking, learning management system, level of online participation, online forum.

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