Soft Computing for Data Mining Applications

K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik (auth.)978-3-540-76994-1, 978-3-540-88044-8

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 fields of Data Mining in combination with Soft Computing methodologies. In these days, the data continues to grow exponentially. 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 stored is growing at a phenomenal rate. As a result, traditional ad hoc mixtures of statistical techniques and data management tools are no longer adequate for analyzing this vast collection of data. Several domains where large volumes of data are stored in centralized or distributed databases includes applications like in electronic commerce, bioinformatics, computer security, Web intelligence, intelligent learning database systems, finance, marketing, healthcare, telecommunications, and other fields.

With the importance of soft computing applied in data mining applications in recent years, this monograph gives a valuable research directions in the field of specialization. As the authors are well known writers in the field of Computer Science and Engineering, the book presents state of the art technology in data mining. The book is very useful to researchers in the field of data mining. – N R Shetty, President, ISTE, India

Table of contents :
Front Matter….Pages –
Introduction….Pages 1-17
Self Adaptive Genetic Algorithms….Pages 19-50
Characteristic Amplification Based Genetic Algorithms….Pages 51-62
Dynamic Association Rule Mining Using Genetic Algorithms….Pages 63-80
Evolutionary Approach for XML Data Mining….Pages 81-118
Soft Computing Based CBIR System….Pages 119-137
Fuzzy Based Neuro – Genetic Algorithm for Stock Market Prediction….Pages 139-166
Data Mining Based Query Processing Using Rough Sets and GAs….Pages 167-195
Hashing the Web for Better Reorganization….Pages 197-215
Algorithms for Web Personalization….Pages 217-230
Classifying Clustered Webpages for Effective Personalization….Pages 231-247
Mining Top – k Ranked Webpages Using SA and GA….Pages 249-258
A Semantic Approach for Mining Biological Databases….Pages 259-278
Probabilistic Approach for DNA Compression….Pages 279-289
Non-repetitive DNA Compression Using Memoization….Pages 291-301
Exploring Structurally Similar Protein Sequence Motifs….Pages 303-318
Matching Techniques in Genomic Sequences for Motif Searching….Pages 319-330
Merge Based Genetic Algorithm for Motif Discovery….Pages 331-341