Data mining : next generation challenges and future directions

Title:

Publication Information:

Menlo Park, California : AAAI Press, 2004

ISBN:

9780262612036

Subject Term:

Data mining

Added Author:

Kargupta, Hillol, 1967-

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Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010127653	QA76.9.D343 D376 2004	Open Access Book	Book	Searching... Unknown

Summary

A state-of-the-art survey of recent advances in data mining or knowledge discovery.

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.

Author Notes

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

Hillol Kargupta and Krishnamoorthy SivakumarAlan Demers and Johannes Gehrke and Mirek RiedewaldJoydeep Ghosh and Alexander Strehl and Srujana MeruguMario Cannataro and Domenico Talia and Paolo TrunfioRobert L. Grossman and Yunhong Gu and Dave Hanley and Xinwei Hong and Jorge Levera and Marco Mazzucco and David Lillethun and Joe Mambretti and Jeremy WeinbergerChris Giannella and Jiawei Han and Jian Pei and Xifeng Yan and Philip S. YuHerve Bronnimann and Bin Chen and Manoranjan Dash and Peter Haas and Peter ScheuermannMilind R. Naphade and Chung-Sheng Li and Thomas S. HuangBhavani ThuraisinghamPaola Sebastiani and Kenneth D. MandlLevent Ertoz and Eric Eilertson and Aleksandar Lazarevic and Pang-Ning Tan and Vipin Kumar and Jaideep Srivastava and Paul DokasDaniel Barbara and James J. Nolan and David Schum and Arun SoodRaymond J. Mooney and Prem Melville and Lappoon Rupert Tang and Jude Shavlik and Ines de Castro Dutra and David Page and Vitor Santos CostaChris Clifton and Murat Kantarcioglu and Jaideep VaidyaRaghu Machiraju and Srinivasan Parthasarathy and John Wilkins and David S. Thompson and Boyd Gatlin and David Richie and Tat-Sang S. Choy and Ming Jiang and Sameep Mehta and Matthew Coatney and Stephen A. Barr and Kaden HazzardMohammed J. Zaki and Jingjing Hu and Chris BystroffMichihiro Kuramochi and Mukund Deshpande and George KarypisNabil R. Adam and Vijayalakshmi Atluri and Dihua Guo and Songmei YuShashi Shekhar and Pusheng Zhang and Yan Huang and Ranga Raju VatsavaiZoran Obradovic and Slobodan VuceticSvetlana Y. Mironova and Michael W. Berry and Scott Atchley and Micah BeckAnupam Joshi and Jeffrey L. UndercofferBettina Berendt and Gerd Stumme and Andreas Hotho

Foreword	p. ix
Preface	p. xiii
Pervasive, Distributed, and Stream Data Mining
1 Existential Pleasures of Distributed Data Mining	p. 3
2 Research Issues in Mining and Monitoring of Intelligence Data	p. 27
3 A Consensus Framework for Integrating Distributed Clusterings under Limited Knowledge Sharing	p. 47
4 Design of Distributed Data Mining Applications on the Knowledge Grid	p. 67
5 Photonic Data Services: Integrating Data, Network and Path Services to Support Next Generation Data Mining Applications	p. 89
6 Mining Frequent Patterns in Data Streams at Multiple Time Granularities	p. 105
7 Efficient Data-Reduction Methods for On-Line Association Rule Discovery	p. 125
8 Discovering Recurrent Events in Multichannel Data Streams Using Unsupervised Methods	p. 147
Counterterrorism, Privacy, and Data Mining
9 Data Mining for Counterterrorism	p. 157
10 Biosurveillance and Outbreak Detection	p. 185
11 MINDS -- Minnesota Intrusion Detection System	p. 199
12 Marshalling Evidence through Data Mining in Support of Counter Terrorism	p. 219
13 Relational Data Mining with Inductive Logic Programming for Link Discovery	p. 239
14 Defining Privacy for Data Mining	p. 255
Scientific Data Mining
15 Mining Temporally-Varying Phenomena in Scientific Datasets	p. 273
16 Methods for Mining Protein Contact Maps	p. 291
17 Mining Scientific Data Sets Using Graphs	p. 315
18 Challenges in Environmental Data Warehousing and Mining	p. 335
19 Trends in Spatial Data Mining	p. 357
20 Challenges in Scientific Data Mining: Heterogenous, Biased, and Large Samples	p. 381
Web, Semantics, and Data Mining
21 Web Mining -- Concepts, Applications, and Research DirectionsJaideep Srivastava and Prasanna Desikan and Vipin Kumar
22 Advancements in Text Mining Algorithms and Software	p. 425
23 On Data Mining, Semantics, and Intrusion Detection, What to Dig for and Where to Find It	p. 437
24 Usage Mining for and on the Semantic Web	p. 461
Bibliography	p. 481
Index	p. 533

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Summary

Author Notes

Table of Contents