Data And Text Mining A Business Applications Approach
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Data and Text Mining
Using worked examples and business case studies, the text answers the four questions: why is data mining important to business and marketing research; how is data mining different from other types of research; what do we learn from data mining; and how do we do data mining?Contents: 1. What Is Data Mining? 2. Traditional Methods. 3. Data-Adaptive Methods. 4. Text Mining. Appendix A: Business Cases. List of Tables. List of Figures. List of Exhibits.
Data Mining for Business Applications
Author: Longbing Cao
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-10-03
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.