Data Mining And Knowledge Discovery (dmkd) Is A Rapidly Expanding Field In Computer Science. It Has Become Very Important Because Of An Increased Demand For Methodologies And Tools That Can Help The Analysis And Understanding Of Huge Amounts Of Data Generated On A Daily Basis By Institutions Like Hospitals, Research Laboratories, Banks, Insurance Companies, And Retail Stores And By Internet Users. This Explosion Is A Result Of The Growing Use Of Electronic Media. But What Is Data Mining (dm)? A Web Search Using The Google Search Engine Retrieves Many (really Many) Definitions Of Data Mining. We Include Here A Few Interesting Ones. One Of The Simpler Definitions Is: “as The Term Suggests, Data Mining Is The Analysis Of Data To Establish Relationships And Identify Patterns” [1]. It Focuses On Identifying Relations In Data. Our Next Example Is More Elaborate: An Information Extraction Activity Whose Goal Is To Discover Hidden Facts Contained In Databases. Using A Combination Of Machine Learning, Statistical Analysis, Modeling Techniques And Database Technology, Data Mining Finds Patterns And Subtle Relationships In Data And Infers Rules That Allow The Prediction Of Future Results. Typical Applications Include Market Segmentation, Customer Profiling, Fraud Detection, Evaluation Of Retail Promotions, And Credit Risk Analysis [2]. Trends In Data Mining And Knowledge Discovery -- Advanced Methods For The Analysis Of Semiconductor Manufacturing Process Data -- Clustering And Visualization Of Retail Market Baskets -- Segmentation Of Continuous Data Streams Based On A Change Detection Methodology -- Instance Selection Using Evolutionary Algorithms: An Experimental Study -- Using Cooperative Coevolution For Data Mining Of Bayesian Networks -- Knowledge Discovery And Data Mining In Medicine -- Satellite Image Classification Using Cascaded Architecture Of Neural Fuzzy Network -- Discovery Of Positive And Negative Rules From Medical Databases Based On Rough Sets. Edited By Nikhil R. Pal, Lakhmi Jain. Information and knowledge in databases is usually hidden & our ability to extract it is limited. The development of techniques to assist in knowledge discovery & validation is becoming increasingly important due to the explosion in internet use & development of powerful sensors resulting in routine generation of terabytes of data needing to be analyzed in order to extract useful knowledge & information. Knowledge, as well as data, plays an important role in every aspect of scientific research. The internet for example provides access to near infinite information in an environment of zero wisdom. Discovering & validating this useful knowledge impacts on the ways businesses operate and the way people work. In presenting the latest and most advanced tools and techniques available for data and web mining strategies, Advanced Techniques in Knowledge Discovery and Data Mining will be welcomed by researchers, engineers and developers involved in knowledge and information management. Advanced Techniques in Knowledge Discovery and Data Mining......Page 1 Advanced Information and Knowledge Processing......Page 3 Contents......Page 5 Preface......Page 6 1. Trends in Data Mining and Knowledge Discovery......Page 12 2. Advanced Methods for the Analysis of Semiconductor Manufacturing Process Data......Page 38 3. Clustering and Visualization of Retail Market Baskets......Page 86 4. Segmentation of Continuous Data Streams Based on a Change Detection Methodology......Page 114 5. Instance Selection Using Evolutionary Algorithms: An Experimental Study......Page 138 6. Using Cooperative Coevolution for Data Mining of Bayesian Networks......Page 164 7. Knowledge Discovery and Data Mining in Medicine......Page 187 8. Satellite Image Classification Using Cascaded Architecture of Neural Fuzzy Network......Page 221 9. Discovery of Positive and Negative Rules from Medical Databases Based on Rough Sets......Page 242 Index......Page 262 "The information explosion has necessitated the development of intelligent tools for extracting useful knowledge from data. This book presents research on some of the most recent advances in data mining and knowledge discovery, and provides the theory as well as its applications on practical real world problems. In addition, the methodologies discussed encompass tools like Bayesian networks as well as major facets of computational intelligence paradigms such as neural networks, evolutionary computing, neuro-fuzzy computing and rough sets." "Advanced Techniques in Data Mining and Knowledge Discovery presents both practical detail and some of the most up-to-date theory in the field, which would be useful for postgraduate students, researchers, application engineers and professors who wish to develop applications using advanced data mining and knowledge discovery techniques."--Jacket Clear and concise explanations to understand the learning paradigms. Chapters written by leading world experts.
