Cover image for Data Mining for Business Applications.
Data Mining for Business Applications.
Title:
Data Mining for Business Applications.
Author:
Soares, C.
ISBN:
9781607506331
Personal Author:
Physical Description:
1 online resource (196 pages)
Series:
Frontiers in Artificial Intelligence and Applications
Contents:
Title page -- Preface -- Members of the Program Committees of the DMBiz Workshops -- Contents -- Data Mining for Business Applications: Introduction -- Part 1. Data Mining Methodology -- Interactivity Closes the Gap - Lessons Learned in an Automotive Industry Application -- Best Practices for Predictive Analytics in B2B Financial Services -- Towards the Generic Framework for Utility Considerations in Data Mining Research -- Customer Validation of Commercial Predictive Models -- Part 2. Data Mining Applications of Today -- Customer Churn Prediction - A Case Study in Retail Banking -- Resource-Bounded Outlier Detection Using Clustering Methods -- An Integrated System to Support Electricity Tariff Contract Definition -- Mining Medical Administrative Data - The PKB Suite -- Part 3. Data Mining Applications of Tomorrow -- Clustering of Adolescent Criminal Offenders Using Psychological and Criminological Profiles -- Forecasting Online Auctions Using Dynamic Models -- A Technology Platform to Enable the Building of Corporate Radar Applications that Mine the Web for Business Insight -- Spatial Data Mining in Practice: Principles and Case Studies -- Subject Index -- Author Index.
Abstract:
Data mining is already incorporated into the business processes in many sectors such as health, retail, automotive, finance, telecom and insurance as well as in government. This technology is well established in applications such as targeted marketing, customer churn detection and market basket analysis. It is also emerging as an important technology in a wide range of new application areas, such as social media, social networks and sensor networks. These areas pose new challenges both in terms of the nature of available data and the underlying support technology. This book contains extended versions of a selection of papers presented at a series of workshops held between 2005 and 2008 on the subject of data mining for business applications. It covers the entire spectrum of issues involved in the development of data mining systems. Areas covered include methodological issues and research challenges, typical problems for which data mining has proved to be an invaluable tool, and innovative applications of data mining which make this an exciting field to work in. The contributions illustrate the importance of maintaining close contact between researchers and practitioners: it is essential that researchers are exposed to and motivated by the real problems and practical constraints experienced by organizations, and practitioners need to interact with the research community to identify new opportunities to apply the latest technology. This book will be of interest not only to data mining researchers and practitioners, but also to students seeking a better understanding of the practical issues involved in building data mining systems.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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