Recent Advances in Data Mining for Categorizing Text Records

W. Chaovalitwongse, Hoang Pham, Seheon Hwang, Z. Liang and C.H. Pham
Additional contact information
W. Chaovalitwongse: Rutgers University
Hoang Pham: Rutgers University
Seheon Hwang: Rutgers University
Z. Liang: Rutgers University
C.H. Pham: Cisco Systems Inc.

Chapter 21 in Recent Advances in Reliability and Quality in Design, 2008, pp 423-440 from Springer

Abstract: Abstract In a world with highly competitive markets, there is a great need in almost all business organizations to develop a highly effective coordination and decision support tool that can be used to become a daily life predictive enterprise to direct, optimize and automate specific decision-making processes. The improved decision-making support can help people to examine data on the past circumstances and present events, as well as project future actions, which will continually improve the quality of products or services. Such improvement has been driven by recent advances in digital data collection and storage technology. The new technology in data collection has resulted in the growth of massive databases, also known as data avalanches. These rapidly growing databases occur in various applications including service industry, global supply chain organizations, air traffic control, nuclear reactors, aircraft fly-by-wire, real time sensor networks, industrial process control, hospital healthcare, and security systems. The massive data, especially text records, on one hand, may contain a great wealth of knowledge and information, but on the other hand, contain other information that may not be reliable due to many uncertainty reasons in our changing environments. However, manually classifying thousands of text records according to their contents can be demanding and overwhelming. Data mining has gained a lot of attention from researchers and practitioners over the past decade as an emerging research area in finding meaningful patterns to make sense out of massive data sets.

Keywords: Support Vector Machine; Data Mining; Association Rule; Text Mining; Rule Induction (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1007/978-1-84800-113-8_21

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