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Data Mining : Next Generation Challenges and Future Directions - Hillol Kargupta

Data Mining

Next Generation Challenges and Future Directions

By: Hillol Kargupta (Editor), Anupama Joshi (Editor), Krishnamoorthy Sivakumar (Editor), Yelena Yesha (Editor)

Paperback Published: 1st October 2004
ISBN: 9780262612036
Number Of Pages: 528
For Ages: 18+ years old

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Data mining, or knowledge discovery, has become an indispensable technology for businesses 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 sets 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 looks at pervasive, distributed, and stream data mining, discussing 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 part considers data mining, counter-terrorism, and privacy concerns, examining such topics as biosurveillance, marshalling evidence through data mining, and link discovery. The third part looks at scientific data mining; 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.

Forewordp. ix
Prefacep. xiii
Pervasive, Distributed, and Stream Data Mining
Existential Pleasures of Distributed Data Miningp. 3
Research Issues in Mining and Monitoring of Intelligence Datap. 27
A Consensus Framework for Integrating Distributed Clusterings under Limited Knowledge Sharingp. 47
Design of Distributed Data Mining Applications on the Knowledge Gridp. 67
Photonic Data Services: Integrating Data, Network and Path Services to Support Next Generation Data Mining Applicationsp. 89
Mining Frequent Patterns in Data Streams at Multiple Time Granularitiesp. 105
Efficient Data-Reduction Methods for On-Line Association Rule Discoveryp. 125
Discovering Recurrent Events in Multichannel Data Streams Using Unsupervised Methodsp. 147
Counterterrorism, Privacy, and Data Mining
Data Mining for Counterterrorismp. 157
Biosurveillance and Outbreak Detectionp. 185
MINDS -- Minnesota Intrusion Detection Systemp. 199
Marshalling Evidence through Data Mining in Support of Counter Terrorismp. 219
Relational Data Mining with Inductive Logic Programming for Link Discoveryp. 239
Defining Privacy for Data Miningp. 255
Scientific Data Mining
Mining Temporally-Varying Phenomena in Scientific Datasetsp. 273
Methods for Mining Protein Contact Mapsp. 291
Mining Scientific Data Sets Using Graphsp. 315
Challenges in Environmental Data Warehousing and Miningp. 335
Trends in Spatial Data Miningp. 357
Challenges in Scientific Data Mining: Heterogenous, Biased, and Large Samplesp. 381
Web, Semantics, and Data Mining
Web Mining -- Concepts, Applications, and Research Directions
Advancements in Text Mining Algorithms and Softwarep. 425
On Data Mining, Semantics, and Intrusion Detection, What to Dig for and Where to Find Itp. 437
Usage Mining for and on the Semantic Webp. 461
Bibliographyp. 481
Indexp. 533
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9780262612036
ISBN-10: 0262612038
Series: American Association for Artificial Intelligence
Audience: Professional
For Ages: 18+ years old
Format: Paperback
Language: English
Number Of Pages: 528
Published: 1st October 2004
Publisher: MIT Press Ltd
Country of Publication: US
Dimensions (cm): 22.9 x 15.2  x 3.2
Weight (kg): 0.79

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