Data Mining: Next Generation Challenges and Future Directions

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Overview

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, ...

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Overview

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.

Product Details

Meet the Author

Krishnamoorthy Sivakumar is Assistant Professor in the School of Electrical Engineering and Computer Science at Washington State University.

Yelena Yesha is Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County.

Anupam Joshi is Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County.

Table of Contents

1 Existential pleasures of distributed data mining 3
2 Research issues in mining and monitoring of intelligence data 27
3 A consensus framework for integrating distributed clusterings under limited knowledge sharing 47
4 Design of distributed data mining applications on the knowledge grid 67
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 105
7 Efficient data-reduction methods for on-line association rule discovery 125
8 Discovering recurrent events in multichannel data streams using unsupervised methods 147
9 Data mining for counterterrorism 157
10 Biosurveillance and outbreak detection 185
11 MINDS - Minnesota intrusion detection system 199
12 Marshalling evidence through data mining in support of counter terrorism 219
13 Relational data mining with inductive logic programming for link discovery 239
14 Defining privacy for data mining 255
15 Mining temporally-varying phenomena in scientific datasets
16 Methods for mining protein contact maps 291
17 Mining scientific data sets using graphs 315
18 Challenges in environmental data warehousing and mining 335
19 Trends in spatial data mining 357
20 Challenges in scientific data mining : heterogeneous, biased, and large samples 381
21 Web mining - concepts, applications, and research directions 405
22 Advancements in text mining algorithms and software 425
23 On data mining, semantics, and intrusion detection : what to dig for and where to find it 437
24 Usage mining for and on the semantic Web 461

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