Foundations and Advances in Data Mining
Contributor(s): Chu, Wesley [editor.] | Lin, Tsau Young [editor.] | SpringerLink (Online service).
Material type:
BookSeries: Studies in Fuzziness and Soft Computing: 180Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.Description: X, 342 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540323938.Subject(s): Computer science | Computers | Artificial intelligence | Information theory | Applied mathematics | Engineering mathematics | Computer Science | Theory of Computation | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics) | Information Systems and Communication Service | Information and Communication, CircuitsDDC classification: 004.0151 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBK7661 |
The Mathematics of Learning -- Logical Regression Analysis: From Mathematical Formulas to Linguistic Rules -- A Feature/Attribute Theory for Association Mining and Constructing the Complete Feature Set -- A New Theoretical Framework for K-means-type Clustering -- Clustering via Decision Tree Construction -- Incremental Mining on Association Rules -- Mining Association Rules from Tabular Data Guided by Maximal Frequent Itemsets -- Sequential Pattern Mining by Pattern-Growth: Principles and Extensions -- Web Page Classification -- Web Mining – Concepts, Applications, and Research Directions -- Privacy-Preserving Data Mining.
With the growing use of information technology and the recent advances in web systems, the amount of data available to users has increased exponentially. Thus, there is a critical need to understand the content of the data. As a result, data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. In this carefully edited volume a theoretical foundation as well as important new directions for data-mining research are presented. It brings together a set of well respected data mining theoreticians and researchers with practical data mining experiences. The presented theories will give data mining practitioners a scientific perspective in data mining and thus provide more insight into their problems, and the provided new data mining topics can be expected to stimulate further research in these important directions.
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