Soft Computing for Data Mining Applications (Studies in Computational Intelligence, 190)

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__ 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.

Evolvable Hardware (EHW) has emerged as a sub-domain of artificial evolution represented by a design methodology (consortium of methods) involving the application of Evolutionary Algorithms (EA) to the synthesis of digital and analogue electronic circuits and systems. Nevertheless, the most benefit for the society and indeed most revolutionizing application of EA is its hardware implementation leading to the EHW. These new EA based methodologies led to a new type of machines that is evolved to attain a desired behaviour, which means they have a behavioural computational intelligence. EHW is a special case of the adaptive hardware, namely being strongly related to the Adaptive Systems (AS) and the Adaptive Hardware (AH). The book presents a careful selection of the field that very well reflects the breadth of this high technology and its terminology and applications in context of the AS/AH. The harmonious symbiosis of the engineering approach and the accurate scientific methodology features the aspects of highly relevant and practical design principles governing the development of EHW and its connections with AS/AH. This book is both attractive and useful for everybody interested in the design and analysis of EHW in context of AS/AH and implementation of real time adaptive hardware hybrid intelligent systems.

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 Data mining consists of attempting to discover novel and useful knowledge from data, trying to find patterns among datasets that can help in intelligent decision making. However, reports of real-world case studies are not generally detailed in the literature, due to the fact that they are usually based on proprietary datasets, making it impossible to publish the results. This kind of situation makes hard to evaluate, in a precise way, the degree of effectiveness of data mining techniques in real-world applications. On the other hand, researchers of this field of expertise usually exploit public-domain datasets. This volume offers a wide spectrum of research work developed for data mining for real-world application. In the following, we give a brief introduction of the chapters that are included in this book. This text presents a selection of the field that reflects the breadth of this high technology and its terminology and applications in context of the AS/AH. It is useful for everybody interested in the design and analysis of EHW in context of AS/AH and implementation of real time adaptive hardware hybrid intelligent systems