Overview
- Authors:
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K. R. Venugopal
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Bangalore University, India
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K. G. Srinivasa
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M.S. Ramaiah Institute of Technology, India
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L. M. Patnaik
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Defence Institute of Advanced Technology, Pune, India
- Recent research in the fields of Data Mining in combination with Soft Computing methodologies
- State-of-the-art technology in data mining
- Includes supplementary material: sn.pub/extras
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Table of contents (18 chapters)
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 1-17
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 19-50
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 51-62
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 63-80
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 81-118
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 119-137
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 139-166
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 167-195
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 197-215
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 217-230
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 231-247
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 249-258
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 259-278
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 279-289
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 291-301
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 303-318
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 319-330
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- K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
Pages 331-341
About this book
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.
Authors and Affiliations
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Bangalore University, India
K. R. Venugopal
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M.S. Ramaiah Institute of Technology, India
K. G. Srinivasa
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Defence Institute of Advanced Technology, Pune, India
L. M. Patnaik