Advanced Techniques in Knowledge Discovery and Data Mining

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Nikhil Pal
Springer Science & Business Media, Dec 31, 2007 - Computers - 256 pages
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Data mining and knowledge discovery (DMKD) is a rapidly expanding field in computer science. It has become very important because of an increased demand for methodologies and tools that can help the analysis and understanding of huge amounts of data generated on a daily basis by institutions like hospitals, research laboratories, banks, insurance companies, and retail stores and by Internet users. This explosion is a result of the growing use of electronic media. But what is data mining (DM)? A Web search using the Google search engine retrieves many (really many) definitions of data mining. We include here a few interesting ones. One of the simpler definitions is: “As the term suggests, data mining is the analysis of data to establish relationships and identify patterns” [1]. It focuses on identifying relations in data. Our next example is more elaborate: An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis [2].
 

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Contents

1 Trends in Data Mining and Knowledge Discovery
1
2 Advanced Methods for the Analysis of Semiconductor Manufacturing Process Data
27
3 Clustering and Visualization of Retail Market Baskets
75
4 Segmentation of Continuous Data Streams Based on a Change Detection Methodology
103
An Experimental Study
127
6 Using Cooperative Coevolution for Data Mining of Bayesian Networks
153
7 Knowledge Discovery and Data Mining in Medicine
176
8 Satellite Image Classification Using Cascaded Architecture of Neural Fuzzy Network
211
9 Discovery of Positive and Negative Rules from Medical Databases Based on Rough Sets
233
Index
253
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Page vii - Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results.
Page 1 - Knowledge discovery in databases (KDD) is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data [1].

About the author (2007)

Jain is director/founder of the Knowledge-Based Intelligent Engineering Systems Centre, located in the Division of Information Technology, Engineering and the Envvironment. He is a fellow of the Institution of Engineers, Australia.

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