Domain Driven Data Mining

This book offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. It bridges the gap between business expectations and research output.

Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from “data-centered pattern mining” to “domain driven actionable knowledge discovery” for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business.

This book constitutes the refereed proceedings of the Third International Conference on Rough Sets and Knowledge Technology, RSKT 2008, held in Chengdu, China, in May 2008. The 91 revised full papers papers presented together with 3 keynote papers and 6 tutorial papers were carefully reviewed and selected from 184 submissions. They all focus on five major research fields: computing theory and paradigms, knowledge technology, intelligent information processing, intelligent control, and applications. The papers are organized in topical sections on rough and soft computing, rough mereology with applications, dominance-based rough set approach, fuzzy-rough hybridization, granular computing, logical and mathematical foundations, formal concept analysis, data mining, machine learning, intelligent information processing, bioinformatics and cognitive informatics, web intelligence, pattern recognition, and real-life applications of knowledge technology.

This book is mainly about an innovative and fundamental method called “intelligent knowledge” to bridge the gap between data mining and knowledge management, two important fields recognized by the information technology (IT) community and business analytics (BA) community respectively. The book includes definitions of the “first-order” analytic process, “second-order” analytic process and intelligent knowledge, which have not formally been addressed by either data mining or knowledge management. Based on these concepts, which are especially important in connection with the current Big Data movement, the book describes a framework of domain-driven intelligent knowledge discovery. To illustrate its technical advantages for large-scale data, the book employs established approaches, such as Multiple Criteria Programming, Support Vector Machine and Decision Tree to identify intelligent knowledge incorporated with human knowledge. The book further shows its applicability by means of real-life data analyses in the contexts of internet business and traditional Chinese medicines.

As information technology continues to advance in massive increments, the bank of information available from personal, financial, and business electronic transactions and all other electronic documentation and data storage is growing at an exponential rate. With this wealth of information comes the opportunity and necessity to utilize this information to maintain competitive advantage and process information effectively in real-world situations. Data Mining and Knowledge Discovery Technologies presents researchers and practitioners in fields such as knowledge management, information science, Web engineering, and medical informatics, with comprehensive, innovative research on data mining methods, structures, tools, and methods, the knowledge discovery process, and data marts, among many other cutting-edge topics.

In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data.