Search results for: Knowledge mining

Authors: S. Sarumathi, N. Shanthi, S. Vidhya, P. Ranjetha

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Data mining idea is mounting rapidly in admiration and also in their popularity. The foremost aspire of data mining method is to extract data from a huge data set into several forms that could be comprehended for additional use. The data mining is a technology that contains with rich potential resources which could be supportive for industries and businesses that pay attention to collect the necessary information of the data to discover their customer’s performances. For extracting data there are several methods are available such as Classification, Clustering, Association, Discovering, and Visualization… etc., which has its individual and diverse algorithms towards the effort to fit an appropriate model to the data. STATISTICA mostly deals with excessive groups of data that imposes vast rigorous computational constraints. These results trials challenge cause the emergence of powerful STATISTICA Data Mining technologies. In this survey an overview of the STATISTICA software is illustrated along with their significant features.

Keywords: Data Mining, STATISTICA Data Miner, Text Miner, Enterprise Server, Classification, Association, Clustering, Regression.

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Authors: Fariba Haghbin, Othman Bin Ibrahim, Mohammad Reza Attarzadeh Niaki

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With the development of virtual communities, there is an increase in the number of members in Virtual Communities (VCs). Many join VCs with the objective of sharing their knowledge and seeking knowledge from others. Despite the eagerness of sharing knowledge and receiving knowledge through VCs, there is no standard of assessing ones knowledge sharing capabilities and prospects of knowledge sharing. This paper developed a vector space model to assess the knowledge sharing prospect of VC users.

Keywords: Knowledge sharing network, Virtual community, knowledge relationship, Vector Space Model.

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Authors: R.M.Rani

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Chess is one of the indoor games, which improves the level of human confidence, concentration, planning skills and knowledge. The main objective of this paper is to help the chess players to improve their chess openings using data mining techniques. Budding Chess Players usually do practices by analyzing various existing openings. When they analyze and correlate thousands of openings it becomes tedious and complex for them. The work done in this paper is to analyze the best lines of Blackmar- Diemer Gambit(BDG) which opens with White D4... using data mining analysis. It is carried out on the collection of winning games by applying association rules. The first step of this analysis is assigning variables to each different sequence moves. In the second step, the sequence association rules were generated to calculate support and confidence factor which help us to find the best subsequence chess moves that may lead to winning position.

Keywords: Blackmar-Diemer Gambit(BDG), Confidence, sequence Association Rules, Support.

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Authors: JunSeok Hwang, Mohsen Gerami

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Knowledge is the foundation for growth and development. Investment in knowledge improves new method for originate knowledge society and knowledge economy. Investment in knowledge embraces expenditure on education and R&D and software. Measuring of investment in knowledge is characteristically complicated. We examine the influence of investment in knowledge in multifactor productivity growth and numbers of patent. We analyze the annual growth of investment in knowledge and we estimate portion of each country intended for produce total investment in knowledge on the whole OECD. We determine the relative efficiency of average patent numbers with average investment in knowledge and we compare GDP growth rates and growth of knowledge investment. The main purpose in this paper is to study to evaluate different aspect, influence and output of investment in knowledge in OECD countries.

Keywords: Knowledge, GDP, Multifactor productivity, Investment, efficiency.

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Authors: B. A. Gobin, R. K. Subramanian

Abstract:

This paper gives an overview of how an OWL ontology has been created to represent template knowledge models defined in CML that are provided by CommonKADS. CommonKADS is a mature knowledge engineering methodology which proposes the use of template knowledge model for knowledge modelling. The aim of developing this ontology is to present the template knowledge model in a knowledge representation language that can be easily understood and shared in the knowledge engineering community. Hence OWL is used as it has become a standard for ontology and also it already has user friendly tools for viewing and editing.

Keywords: Ontology, OWL, Template Knowledge Models, CommonKADS

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Authors: M. Awawdeh, A. Fedi

Abstract:

The application of data mining to environmental monitoring has become crucial for a number of tasks related to emergency management. Over recent years, many tools have been developed for decision support system (DSS) for emergency management. In this article a graphical user interface (GUI) for environmental monitoring system is presented. This interface allows accomplishing (i) data collection and observation and (ii) extraction for data mining. This tool may be the basis for future development along the line of the open source software paradigm.

Keywords: Data Mining, Environmental data, Mathematical Models, Matlab Graphical User Interface.

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Authors: First G.M. Karthik, Second Ramachandra.V.Pujeri, Dr.

Abstract:

Generalized Center String (GCS) problem are generalized from Common Approximate Substring problem and Common substring problems. GCS are known to be NP-hard allowing the problems lies in the explosion of potential candidates. Finding longest center string without concerning the sequence that may not contain any motifs is not known in advance in any particular biological gene process. GCS solved by frequent pattern-mining techniques and known to be fixed parameter tractable based on the fixed input sequence length and symbol set size. Efficient method known as Bpriori algorithms can solve GCS with reasonable time/space complexities. Bpriori 2 and Bpriori 3-2 algorithm are been proposed of any length and any positions of all their instances in input sequences. In this paper, we reduced the time/space complexity of Bpriori algorithm by Constrained Based Frequent Pattern mining (CBFP) technique which integrates the idea of Constraint Based Mining and FP-tree mining. CBFP mining technique solves the GCS problem works for all center string of any length, but also for the positions of all their mutated copies of input sequence. CBFP mining technique construct TRIE like with FP tree to represent the mutated copies of center string of any length, along with constraints to restraint growth of the consensus tree. The complexity analysis for Constrained Based FP mining technique and Bpriori algorithm is done based on the worst case and average case approach. Algorithm's correctness compared with the Bpriori algorithm using artificial data is shown.

Keywords: Constraint Based Mining, FP tree, Data mining, GCS problem, CBFP mining technique.

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Authors: J. Alamelu Mangai, Satej Wagle, V. Santhosh Kumar

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The exponential increase in the volume of medical image database has imposed new challenges to clinical routine in maintaining patient history, diagnosis, treatment and monitoring. With the advent of data mining and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. In this research a medical image classification framework using data mining techniques is proposed. It involves feature extraction, feature selection, feature discretization and classification. In the classification phase, the performance of the traditional kNN k nearest neighbor classifier is improved using a feature weighting scheme and a distance weighted voting instead of simple majority voting. Feature weights are calculated using the interestingness measures used in association rule mining. Experiments on the retinal fundus images show that the proposed framework improves the classification accuracy of traditional kNN from 78.57 % to 92.85 %.

Keywords: Medical Image Mining, Data Mining, Feature Weighting, Association Rule Mining, k nearest neighbor classifier.

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Authors: Wadii Boulila, Imed Riadh Farah, Karim Saheb Ettabaa, Basel Solaiman, Henda Ben Ghezala

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This paper investigates the problem of tracking spa¬tiotemporal changes of a satellite image through the use of Knowledge Discovery in Database (KDD). The purpose of this study is to help a given user effectively discover interesting knowledge and then build prediction and decision models. Unfortunately, the KDD process for spatiotemporal data is always marked by several types of imperfections. In our paper, we take these imperfections into consideration in order to provide more accurate decisions. To achieve this objective, different KDD methods are used to discover knowledge in satellite image databases. Each method presents a different point of view of spatiotemporal evolution of a query model (which represents an extracted object from a satellite image). In order to combine these methods, we use the evidence fusion theory which considerably improves the spatiotemporal knowledge discovery process and increases our belief in the spatiotemporal model change. Experimental results of satellite images representing the region of Auckland in New Zealand depict the improvement in the overall change detection as compared to using classical methods.

Keywords: Knowledge discovery in satellite databases, knowledge fusion, data imperfection, data mining, spatiotemporal change detection.

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Authors: Sung Ho Ha, Seong Hyeon Joo

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Data mining techniques have been used in medical research for many years and have been known to be effective. In order to solve such problems as long-waiting time, congestion, and delayed patient care, faced by emergency departments, this study concentrates on building a hybrid methodology, combining data mining techniques such as association rules and classification trees. The methodology is applied to real-world emergency data collected from a hospital and is evaluated by comparing with other techniques. The methodology is expected to help physicians to make a faster and more accurate classification of chest pain diseases.

Keywords: Data mining, medical decisions, medical domainknowledge, chest pain.

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Authors: B. A. Gobin, R. K. Subramanian

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Knowledge modelling, a main activity for the development of Knowledge Based Systems, have no set standards and are mostly done in an ad hoc way. There is a lack of support for the transition from abstract level to implementation. In this paper, a methodology for the development of the knowledge model, which is inspired by both Software and Knowledge Engineering, is proposed. Use of UML which is the de-facto standard for modelling in the software engineering arena is explored for knowledge modelling. The methodology proposed, is used to develop a knowledge model of a knowledge based system for recommending suitable hotels for tourists visiting Mauritius.

Keywords: Domain Modelling, Knowledge Based Systems, Knowledge Modelling, UML.

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Authors: M. W. Yip, H. H. Ng, S. Din, N. Abu Bakar

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This paper reviewed the relationships between the Knowledge Management (KM) activities and its perceived benefits in the knowledge based organisations. KM activities include: knowledge identification, knowledge acquisition, knowledge application, knowledge sharing, knowledge creation and knowledge preservation. And the perceived benefits of KM are fast customer responsiveness, operation excellence and high innovative intensity.  Based on the above review, a conceptual framework for KM implementation in retail business organisations has been proposed. Finally the paper forwarded some limitations of the framework and based on which, directions for future research had been suggested.

Keywords: Knowledge Management, Knowledge Management Activities, Retail Business, Knowledge Economy.

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Authors: P. Sengottuvelan, T. Gopalakrishnan

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Web Usage Mining is the application of data mining techniques to find usage patterns from web log data, so as to grasp required patterns and serve the requirements of Web-based applications. User’s expertise on the internet may be improved by minimizing user’s web access latency. This may be done by predicting the future search page earlier and the same may be prefetched and cached. Therefore, to enhance the standard of web services, it is needed topic to research the user web navigation behavior. Analysis of user’s web navigation behavior is achieved through modeling web navigation history. We propose this technique which cluster’s the user sessions, based on the K-medoids technique.

Keywords: Clustering, K-medoids, Recommendation, User Session, Web Usage Mining.

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Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: Social Media, text mining, knowledge discovery, predictive analysis, machine learning.

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Authors: Dewan Md. Farid, Nouria Harbi, Suman Ahmmed, Md. Zahidur Rahman, Chowdhury Mofizur Rahman

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Network security attacks are the violation of information security policy that received much attention to the computational intelligence society in the last decades. Data mining has become a very useful technique for detecting network intrusions by extracting useful knowledge from large number of network data or logs. Naïve Bayesian classifier is one of the most popular data mining algorithm for classification, which provides an optimal way to predict the class of an unknown example. It has been tested that one set of probability derived from data is not good enough to have good classification rate. In this paper, we proposed a new learning algorithm for mining network logs to detect network intrusions through naïve Bayesian classifier, which first clusters the network logs into several groups based on similarity of logs, and then calculates the prior and conditional probabilities for each group of logs. For classifying a new log, the algorithm checks in which cluster the log belongs and then use that cluster-s probability set to classify the new log. We tested the performance of our proposed algorithm by employing KDD99 benchmark network intrusion detection dataset, and the experimental results proved that it improves detection rates as well as reduces false positives for different types of network intrusions.

Keywords: Clustering, detection rate, false positive, naïveBayesian classifier, network intrusion detection.

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Authors: Magdalena Pejas

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This article concerns the presentation of an integrated method for detection of steganographic content embedded by new unknown programs. The method is based on data mining and aggregated hypothesis testing. The article contains the theoretical basics used to deploy the proposed detection system and the description of improvement proposed for the basic system idea. Further main results of experiments and implementation details are collected and described. Finally example results of the tests are presented.

Keywords: Steganography, steganalysis, data embedding, data mining, feature extraction, knowledge base, system learning, hypothesis testing, error estimation, black box program, file structure.

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Authors: Donna R. Tabangin, Jacqueline C. Flores, Nelson F. Emperador

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Information is power. Geographical information is an emerging science that is advancing the development of knowledge to further help in the understanding of the relationship of “place" with other disciplines such as crime. The researchers used crime data for the years 2004 to 2007 from the Baguio City Police Office to determine the incidence and actual locations of crime hotspots. Combined qualitative and quantitative research methodology was employed through extensive fieldwork and observation, geographic visualization with Geographic Information Systems (GIS) and Global Positioning Systems (GPS), and data mining. The paper discusses emerging geographic visualization and data mining tools and methodologies that can be used to generate baseline data for environmental initiatives such as urban renewal and rejuvenation. The study was able to demonstrate that crime hotspots can be computed and were seen to be occurring to some select places in the Central Business District (CBD) of Baguio City. It was observed that some characteristics of the hotspot places- physical design and milieu may play an important role in creating opportunities for crime. A list of these environmental attributes was generated. This derived information may be used to guide the design or redesign of the urban environment of the City to be able to reduce crime and at the same time improve it physically.

Keywords: Crime mapping, data mining, environmental design, geographic visualization, GIS.

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Authors: S. Sarumathi, N. Shanthi

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Due to the fast and flawless technological innovation there is a tremendous amount of data dumping all over the world in every domain such as Pattern Recognition, Machine Learning, Spatial Data Mining, Image Analysis, Fraudulent Analysis, World Wide Web etc., This issue turns to be more essential for developing several tools for data mining functionalities. The major aim of this paper is to analyze various tools which are used to build a resourceful analytical or descriptive model for handling large amount of information more efficiently and user friendly. In this survey the diverse tools are illustrated with their extensive technical paradigm, outstanding graphical interface and inbuilt multipath algorithms in which it is very useful for handling significant amount of data more indeed.

Keywords: Classification, Clustering, Data Mining, Machine learning, Visualization.

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Authors: Kunyanuth Kularbphettong, Phanu Waraporn, Cholticha Tongsiri

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This research aims to create a model for analysis of student motivation behavior on e-Learning based on association rule mining techniques in case of the Information Technology for Communication and Learning Course at Suan Sunandha Rajabhat University. The model was created under association rules, one of the data mining techniques with minimum confidence. The results showed that the student motivation behavior model by using association rule technique can indicate the important variables that influence the student motivation behavior on e-Learning.

Keywords: Motivation behavior, e-learning, moodle log, association rule mining.

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Authors: Chih-Wei Lin, Duen-Ren Liu, Hui-Fang Chen

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Effective knowledge support relies on providing operation-relevant knowledge to workers promptly and accurately. A knowledge flow represents an individual-s or a group-s knowledge-needs and referencing behavior of codified knowledge during operation performance. The flow has been utilized to facilitate organizational knowledge support by illustrating workers- knowledge-needs systematically and precisely. However, conventional knowledge-flow models cannot work well in cooperative teams, which team members usually have diverse knowledge-needs in terms of roles. The reason is that those models only provide one single view to all participants and do not reflect individual knowledge-needs in flows. Hence, we propose a role-based knowledge-flow view model in this work. The model builds knowledge-flow views (or virtual knowledge flows) by creating appropriate virtual knowledge nodes and generalizing knowledge concepts to required concept levels. The customized views could represent individual role-s knowledge-needs in teamwork context. The novel model indicates knowledge-needs in condensed representation from a roles perspective and enhances the efficiency of cooperative knowledge support in organizations.

Keywords: cooperative knowledge support, knowledge flow, knowledge-flow view, role-based models

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Authors: Mária Stachová, Lukáš Sobíšek

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Data mining incorporates a group of statistical methods used to analyze a set of information, or a data set. It operates with models and algorithms, which are powerful tools with the great potential. They can help people to understand the patterns in certain chunk of information so it is obvious that the data mining tools have a wide area of applications. For example in the theoretical chemistry data mining tools can be used to predict moleculeproperties or improve computer-assisted drug design. Classification analysis is one of the major data mining methodologies. The aim of thecontribution is to create a classification model, which would be able to deal with a huge data set with high accuracy. For this purpose logistic regression, Bayesian logistic regression and random forest models were built using R software. TheBayesian logistic regression in Latent GOLD software was created as well. These classification methods belong to supervised learning methods. It was necessary to reduce data matrix dimension before construct models and thus the factor analysis (FA) was used. Those models were applied to predict the biological activity of molecules, potential new drug candidates.

Keywords: data mining, classification, drug design, QSAR

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Authors: Iftikhar U. Sikder

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This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.

Keywords: Concept approximation, granular computing, reducts, rough set theory, rule induction.

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Authors: A. Argiri, A. Molla, Tzouvalekas, E. Skoufogianni, N. Danalatos

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The release of heavy metals into the environment has increased over the last years. In this study, 25 soil samples (0-15 cm) from the fields near the mining area in Drama region were selected. The samples were analyzed in the laboratory for their physicochemical properties and for seven “pseudo-total’’ heavy metals content, namely Pb, Zn, Cd, Cr, Cu, Ni, and Mn. The total metal concentrations (Pb, Zn, Cd, Cr, Cu, Ni and Mn) in digests were determined by using the atomic absorption spectrophotometer. According to the results, the mean concentration of the listed heavy metals in 25 soil samples are Cd 1.1 mg/kg, Cr 15 mg/kg, Cu 21.7 mg/kg, Ni 30.1 mg/kg, Pd 50.8 mg/kg, Zn 99.5 mg/kg and Mn 815.3 mg/kg. The results show that the heavy metals remain in the soil even if the mining closed many years ago.

Keywords: Greece, heavy metals, mining, pollution

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Authors: M. Kargar, F. Gharbalchi

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In this paper a new method is suggested for distributed data-mining by the probability patterns. These patterns use decision trees and decision graphs. The patterns are cared to be valid, novel, useful, and understandable. Considering a set of functions, the system reaches to a good pattern or better objectives. By using the suggested method we will be able to extract the useful information from massive and multi-relational data bases.

Keywords: Data-mining, Decision tree, Decision graph, Pattern, Relationship.

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Authors: Pairaya J., Buddhagarn R., Sukree S., Punthumadee K.

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Application of Information Technology (IT) has revolutionized the functioning of business all over the world. Its impact has been felt mostly among the information of dependent industries. Tourism is one of such industry. The conceptual framework in this study represents an innovation of travel information searching system on mobile devices which is used as tools to deliver travel information (such as hotels, restaurants, tourist attractions and souvenir shops) for each user by travelers segmentation based on data mining technique to segment the tourists- behavior patterns then match them with tourism products and services. This system innovation is designed to be a knowledge incremental learning. It is a marketing strategy to support business to respond traveler-s demand effectively.

Keywords: Tourism, Innovation, Information Searching, Data Mining.

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Authors: Heba Mohammed Nagy, Walid Mohamed Aly, Osama Fathy Hegazy

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Educational  data mining  is  a  specific  data   mining field applied to data originating from educational environments, it relies on different  approaches to discover hidden knowledge  from  the  available   data. Among these approaches are   machine   learning techniques which are used to build a system that acquires learning from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems.

In  our  research, we propose  a “Student  Advisory  Framework” that  utilizes  classification  and  clustering  to  build  an  intelligent system. This system can be used to provide pieces of consultations to a first year  university  student to  pursue a  certain   education   track   where  he/she  will  likely  succeed  in, aiming  to  decrease   the  high  rate   of  academic  failure   among these  students.  A real case study  in Cairo  Higher  Institute  for Engineering, Computer  Science  and  Management  is  presented using  real  dataset   collected  from  2000−2012.The dataset has two main components: pre-higher education dataset and first year courses results dataset. Results have proved the efficiency of the suggested framework.

Keywords: Classification, Clustering, Educational Data Mining (EDM), Machine Learning.

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Authors: S. Kittipongvises, A. Dubsok

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The mineral and mining industry is necessary for countries to have an adequate and reliable supply of materials to meet their socio-economic development. Despite its importance, the environmental impacts from mineral exploration are hugely significant. This study aimed to investigate and quantify the amount of GHGs emissions emitted from both electricity and diesel vehicle fuel consumption in basalt mining in Thailand. Plant A, located in the northeastern region of Thailand, was selected as a case study. Results indicated that total GHGs emissions from basalt mining and operation (Plant A) were approximately 2,501,086 kgCO₂e and 1,997,412 kgCO₂e in 2014 and 2015, respectively. The estimated carbon intensity ranged between 1.824 kgCO₂e to 2.284 kgCO₂e per ton of rock product. Scope 1 (direct emissions) was the dominant driver of its total GHGs compared to scope 2 (indirect emissions). As such, transport related combustion of diesel fuels generated the highest GHGs emission (65%) compared to emissions from purchased electricity (35%). Some of the potential implications for mining entities were also presented.

Keywords: Basalt mining, diesel fuel, electricity, GHGs emissions, Thailand.

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Authors: Nevena Stolba, A Min Tjoa

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Evidence-based medicine is a new direction in modern healthcare. Its task is to prevent, diagnose and medicate diseases using medical evidence. Medical data about a large patient population is analyzed to perform healthcare management and medical research. In order to obtain the best evidence for a given disease, external clinical expertise as well as internal clinical experience must be available to the healthcare practitioners at right time and in the right manner. External evidence-based knowledge can not be applied directly to the patient without adjusting it to the patient-s health condition. We propose a data warehouse based approach as a suitable solution for the integration of external evidence-based data sources into the existing clinical information system and data mining techniques for finding appropriate therapy for a given patient and a given disease. Through integration of data warehousing, OLAP and data mining techniques in the healthcare area, an easy to use decision support platform, which supports decision making process of care givers and clinical managers, is built. We present three case studies, which show, that a clinical data warehouse that facilitates evidence-based medicine is a reliable, powerful and user-friendly platform for strategic decision making, which has a great relevance for the practice and acceptance of evidence-based medicine.

Keywords: data mining, data warehousing, decision-support systems, evidence-based medicine.

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Authors: G. Poonkuzhali, K.Thiagarajan, K.Sarukesi, G.V.Uma

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The emergence of the Internet has brewed the revolution of information storage and retrieval. As most of the data in the web is unstructured, and contains a mix of text, video, audio etc, there is a need to mine information to cater to the specific needs of the users without loss of important hidden information. Thus developing user friendly and automated tools for providing relevant information quickly becomes a major challenge in web mining research. Most of the existing web mining algorithms have concentrated on finding frequent patterns while neglecting the less frequent ones that are likely to contain outlying data such as noise, irrelevant and redundant data. This paper mainly focuses on Signed approach and full word matching on the organized domain dictionary for mining web content outliers. This Signed approach gives the relevant web documents as well as outlying web documents. As the dictionary is organized based on the number of characters in a word, searching and retrieval of documents takes less time and less space.

Keywords: Outliers, Relevant document, , Signed Approach, Web content mining, Web documents..

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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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