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...

Πίνακας περιεχομένων:

• 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.