Data Mining and Its Applications for Knowledge Management: A Literature Review from 2007 to 2012
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In this article, the authors explored the applications of data mining techniques which have been developed to support knowledge management process and classified the journal articles indexed in ScienceDirect Database from 2007 to 2012.Abstract:
Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming decades. This review paper explores the applications of data mining techniques which have been developed to support knowledge management process. The journal articles indexed in ScienceDirect Database from 2007 to 2012 are analyzed and classified. The discussion on the findings is divided into 4 topics: (i) knowledge resource; (ii) knowledge types and/or knowledge datasets; (iii) data mining tasks; and (iv) data mining techniques and applications used in knowledge management. The article first briefly describes the definition of data mining and data mining functionality. Then the knowledge management rationale and major knowledge management tools integrated in knowledge management cycle are described. Finally, the applications of data mining techniques in the process of knowledge management are summarized and discussed.read more
Citations
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Proceedings ArticleDOI
Improving Heart Disease Prediction Using Feature Selection Approaches
TL;DR: This research focuses on feature selection techniques and algorithms where multiple heart disease datasets are used for experimentation analysis and to show the accuracy improvement.
Journal ArticleDOI
Selection criteria for text mining approaches
TL;DR: A set of criteria to evaluate the effectiveness of text mining techniques in an attempt to facilitate the selection of appropriate technique is proposed.
Journal ArticleDOI
Cuckoo Search Optimized Reduction and Fuzzy Logic Classifier for Heart Disease and Diabetes Prediction
TL;DR: This paper proposes a diagnosis system using cuckoo search optimized rough sets based attribute reduction and fuzzy logic system which outperforms the existing approaches to classify heart and diabetes diseases.
Journal ArticleDOI
Application of knowledge discovery process on the prediction of stroke
TL;DR: The findings of the current study pointed out that ANN had more predictive performance when compared with SVM in predicting stroke and the proposed ANN model would be useful when making clinical decisions regarding stroke.
Journal ArticleDOI
Engineering the Value Network of the Customer Interface and Marketing in the Data-Rich Retail Environment
Key Pousttchi,Yvonne Hufenbach +1 more
TL;DR: This paper provides the first systematic analysis of the implications of the virtual world market players Apple, Google, Facebook, eBay/PayPal, and Amazon on traditional retailers' business.
References
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Journal ArticleDOI
From Data Mining to Knowledge Discovery in Databases
TL;DR: An overview of this emerging field is provided, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases.
Data Mining: Concepts and Techniques (2nd edition)
Jiawei Han,Micheline Kamber +1 more
TL;DR: There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99].
Book
Knowledge Management in Theory and Practice
TL;DR: This second edition of this textbook and professional reference offers a comprehensive overview of the field of KM, providing both a substantive theoretical grounding and a pragmatic approach to applying key concepts.
Journal ArticleDOI
Review: Application of data mining techniques in customer relationship management: A literature review and classification
TL;DR: Findings of this paper indicate that the research area of customer retention received most research attention and classification and association models are the two commonly used models for data mining in CRM.