Soft computing for data mining applications
Author(s)
Bibliographic Information
Soft computing for data mining applications
（Studies in computational intelligence, v. 190）
Springer, c2009
Available at / 3 libraries
-
No Libraries matched.
- Remove all filters.
Note
Includes bibliographical references
Description and Table of Contents
Description
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.
Table of Contents
Self Adaptive Genetic Algorithms.- Characteristic Amplification Based Genetic Algorithms.- Dynamic Association Rule Mining Using Genetic Algorithms.- Evolutionary Approach for XML Data Mining.- Soft Computing Based CBIR System.- Fuzzy Based Neuro - Genetic Algorithm for Stock Market Prediction.- Data Mining Based Query Processing Using Rough Sets and GAs.- Hashing the Web for Better Reorganization.- Algorithms for Web Personalization.- Classifying Clustered Webpages for Effective Personalization.- Mining Top - k Ranked Webpages Using SA and GA.- A Semantic Approach for Mining Biological Databases.- Probabilistic Approach for DNA Compression.- Non-repetitive DNA Compression Using Memoization.- Exploring Structurally Similar Protein Sequence Motifs.- Matching Techniques in Genomic Sequences for Motif Searching.- Merge Based Genetic Algorithm for Motif Discovery.
by "Nielsen BookData"
