Contemporary Perspectives in Data Mining, Volume 1
1st Edition
Published on 1. April 2013
255 pages
978-1-62396-057-5 (ISBN)
Description
The series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner.
Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted form this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups.
Data mining applications are seen in finance (banking, brokerage, insurance), marketing (customer relationships, retailing, logistics, travel), as well as in manufacturing, health care, fraud detection, home-land security, and law enforcement.
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12/2012
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Content
- Cover
- Series page
- Contemporary Perspectives in Data Mining
- Library of Congress Cataloging-in-Publication Data
- Contents
- Section A: Methodological Studies
- Chapter 1: Frame Selection Based on Mixtures of Trees in Discrete Data
- Chapter 2: Data Mining Techniques for Quality Improvement
- Chapter 3: Big Bang Data Generation
- Chapter 4: Business Analytics-Today's Green?
- Chapter 5: Change Point Plots
- Section B: Financial Studies
- Chapter 6: Discovering the Comovement Structure of Chinese Stock Market by SPACE with EM Algorithm
- Chapter 7: Knowledge Discovery for Continuous Financial Assurance Using Multiple Types of Digital Information
- Chapter 8: Regression Estimation of a Cost Function with Severe Data Problems and Extreme Values of Observations in the Maintenance and Repair Activities of Backbone Internet Providers
- Section C: Behavioral Studies
- Chapter 9: Data Mining's Usefulness For Assessing Market Segmentation Performance
- Chapter 10: Measuring the Semantic and Representational Consistency of Interconnected Structured and Unstructured Data for Data Mining Applications
- Chapter 11: Clustering and Principal Components Analyses to Understand Student Motivations and Ethical Approaches to Academic Ethics with Recommendations for Curricular Change
- About the Editor
