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Data Mining, or Knowledge Discovery, has become an indispensable technology for business and researchers in many fields. Drawing on work in such areas as statistics, machine learning, pattern recognition, databases, and high performance computing, data mining extracts useful information from the large data set now available to industry and science. This collection surveys the most recent advances in the field and charts directions for future research. The first part discusses topics that include distributed data mining algorithms for new application areas, several aspects of next-generation data mining systems and applications, and detection of recurrent patterns in digital media. The second examines such topics as bio-surveillance, marshalling evidence through data mining, and link discovery. The third focuses at scientific data mining; and the topics include mining temporally-varying phenomena, data sets using graphs, and spatial data mining. The last part considers web, semantics and data mining, examining advances in text mining algorithms and software, semantic webs, and other subjects. The book serves as a supplementary text for the students of Information Technology. It should also be of interest to the professionals of knowledge management. Table of Contents Foreword. Preface Pervasive, Distributed, and Stream Data Mining- 1. Existential Pleasures of Distributed Data Mining 2. Research Issues in Mining and Monitoring of Intelligence Data. 3. A Consensus Framework for Integrating Distributed Clusterings Under Limited Knowledge Sharing. 4. Design of Distributed Data Mining Applications on the Knowledge Grid. 5. Photonic Data Services: Integrating Data, Network and Path Services to Support Next Generation Data Mining Applications. 6. Mining Frequent Patterns in Data Streams at Multiple Time Granularities. 7. Efficient Data-Reduction Methods for On-Line Association Rule Discovery. 8. Discovering Recurrent Events in Multichannel Data Streams Using Unsupervised Methods. Counterterrorism, Privacy, and Data Mining- 9. Data Mining for Counterterrorism. 10.Biosurveillance and Outbreak Detection. 11.MINDS-Minnesota Intrusion Detection System. 12.Marshalling Evidence Through Data Mining in Support of Counter Terrorism. 13.Relational Data Mining with Inductive Logic Programming for Link Discovery. 14.Defining Privacy for Data Mining Scientific Data Mining- 15.Mining Temporally-Varying Phenomena in Scientific Datasets. 16.Methods for Mining Protein Contact Maps 17.Mining Scientific Data Sets using Graphs. 18.Challenges in Environmental Data Warehousing and Mining. 19.Trends in Spatial Data Mining. 20.Challenges in Scientific Data Mining: Heterogeneous, Biased, and Large Samples. Web, Semantics, and Data Mining- 21.Web Mining-Concepts, Applications, and Research Directions. 22.Advancements in Text Mining Algorithms and Software. 23.On Data Mining, Semantics, and Intrusion Detection: What to Dig for and Where to Find It. 24.Usage Mining for and on the Semantic Web Bibliography. Index.