Advanced techniques in knowledge discovery and data mining /

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Bibliographic Details
Imprint:New York : Springer-Verlag, 2004.
Description:1 online resource (xii, 254 p.) : ill.
Language:English
Series:Advanced information and knowledge processing
Advanced information and knowledge processing.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/8876399
Hidden Bibliographic Details
Other authors / contributors:Pal, Nikhil R.
Jain, L. C.
ISBN:1852338679 (alk. paper)
9781852338671 (alk. paper)
9781846281839
1846281830
6611180605
9786611180607
Notes:Includes bibliographical references and index.
Description based on print version record.
Summary:"The information explosion has necessitated the development of intelligent tools for extracting useful knowledge from data. This book presents research on some of the most recent advances in data mining and knowledge discovery, and provides the theory as well as its applications on practical real world problems. In addition, the methodologies discussed encompass tools like Bayesian networks as well as major facets of computational intelligence paradigms such as neural networks, evolutionary computing, neuro-fuzzy computing and rough sets." "Advanced Techniques in Data Mining and Knowledge Discovery presents both practical detail and some of the most up-to-date theory in the field, which would be useful for postgraduate students, researchers, application engineers and professors who wish to develop applications using advanced data mining and knowledge discovery techniques."--Jacket.
Other form:Print version: Advanced techniques in knowledge discovery and data mining. New York : Springer-Verlag, 2004 1852338679 9781852338671
Description
Summary: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].
Physical Description:1 online resource (xii, 254 p.) : ill.
Bibliography:Includes bibliographical references and index.
ISBN:1852338679 (alk. paper)
9781852338671 (alk. paper)
9781846281839
1846281830
6611180605
9786611180607