Soft Computing for Data Mining Applications

• Κύριοι συγγραφείς: Venugopal, K. R. (Συγγραφέας), Srinivasa, K. G. (Συγγραφέας), Patnaik, L. M. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.
• Σειρά: Studies in Computational Intelligence, 190
• Θέματα: Computer science.; Artificial intelligence.; Computer-aided engineering.; Applied mathematics.; Engineering mathematics.; Computer Science.; Computer-Aided Engineering (CAD, CAE) and Design.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).

 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. 

 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.