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Soft Computing for Data Mining Applications

Springer Berlin,
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This monograph presents recent research in the fields of data mining in combination with soft computing methodologies. It provides valuable research directions in the field of specialization.


Titel: Soft Computing for Data Mining Applications
Autoren/Herausgeber: K. R. Venugopal, K.G. Srinivasa, L. M. Patnaik
Aus der Reihe: Studies in Computational Intelligence
Ausgabe: Softcover reprint of hardcover 1st ed. 2009

ISBN/EAN: 9783642101250

Seitenzahl: 341
Format: 23,5 x 15,5 cm
Produktform: Taschenbuch/Softcover
Gewicht: 557 g
Sprache: Englisch

The authors have consolidated their research work in this volume titled Soft Computing for Data Mining Applications. The monograph gives an insight into the research in the ?elds of Data Mining in combination with Soft Computing methodologies. In these days, the data continues to grow - ponentially. Much of the data is implicitly or explicitly imprecise. Database discovery seeks to discover noteworthy, unrecognized associations between the data items in the existing database. The potential of discovery comes from the realization that alternate contexts may reveal additional valuable information. The rate at which the data is storedis growing at a phenomenal rate. Asaresult,traditionaladhocmixturesofstatisticaltechniquesanddata managementtools are no longer adequate for analyzing this vast collection of data. Severaldomainswherelargevolumesofdataarestoredincentralizedor distributeddatabasesincludesapplicationslikeinelectroniccommerce,bio- formatics, computer security, Web intelligence, intelligent learning database systems,?nance,marketing,healthcare,telecommunications,andother?elds. E?cient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the ca- bility of computers to search huge amounts of data in a fast and e?ective manner. However,the data to be analyzed is imprecise and a?icted with - certainty. In the case of heterogeneous data sources such as text and video, the data might moreover be ambiguous and partly con?icting. Besides, p- terns and relationships of interest are usually approximate. Thus, in order to make the information mining process more robust it requires tolerance toward imprecision, uncertainty and exceptions. - Newsletter
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