Data Mining: Next Generation Challenges and Future Directions (Paperback)

Hillol Kargupta, Anupam Joshi, Krishnamoorthy Sivakumar, Yelena Yesha

  • 出版商: AAAI Press
  • 出版日期: 2004-11-19
  • 售價: $1,320
  • 貴賓價: 9.5$1,254
  • 語言: 英文
  • 頁數: 528
  • 裝訂: Paperback
  • ISBN: 0262612038
  • ISBN-13: 9780262612036
  • 相關分類: Data-mining 資料探勘
  • 立即出貨(限量) (庫存=2)




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.

Hillol Kargupta is Associate 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.

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.



Table of Contents:

Foreword ix
Preface xiii
Pervasive, Distributed, and Stream Data Mining
1 Existential Pleasures of Distributed Data Mining
Hillol Kargupta and Krishnamoorthy Sivakumar
2 Research Issues in Mining and Monitoring of Intelligence Data
Alan Demers, Johannes Gehrke and Mirek Riedewald
3 A Consensus Framework for Integrating Distributed Clusterings under Limited Knowledge Sharing
Joydeep Ghosh, Alexander Strehl and Srujana Merugu
4 Design of Distributed Data Mining Applications on the Knowledge Grid
Mario Cannataro, Domenico Talia and Paolo Trunfio
5 Photonic Data Services: Integrating Data, Network and Path Services to Support Next Generation Data Mining Applications
Robert L. Grossman, Yunhong Gu, Dave Hanley, Xinwei Hong, Jorge Levera, Marco Mazzucco, David Lillethun, Joe Mambretti and Jeremy Weinberger
6 Mining Frequent Patterns in Data Streams at Multiple Time Granularities
Chris Giannella, Jiawei Han, Jian Pei, Xifeng Yan and Philip S. Yu
7 Efficient Data-Reduction Methods for On-Line Association Rule Discovery
Hervé Brönnimann, Bin Chen, Manoranjan Dash, Peter Haas and Peter Scheuermann
8 Discovering Recurrent Events in Multichannel Data Streams Using Unsupervised Methods
Milind R. Naphade, Chung-Sheng Li and Thomas S. Huang
Counterterrorism, Privacy, and Data Mining
9 Data Mining for Counterterrorism
Bhavani Thuraisingham
10 Biosurveillance and Outbreak Detection
Paola Sebastiani and Kenneth D. Mandl
11 MINDS -- Minnesota Intrusion Detection System
Levent Ertöz, Eric Eilertson, Aleksandar Lazarevic, Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava and Paul Dokas
12 Marshalling Evidence through Data Mining in Support of Counter Terrorism
Daniel Barbará, James J. Nolan, David Schum and Arun Sood
13 Relational Data Mining with Inductive Logic Programming for Link Discovery
Raymond J. Mooney, Prem Melville, Lappoon Rupert Tang, Jude Shavlik, Inês de Castro Dutra, David Page and Vítor Santos Costa
14 Defining Privacy for Data Mining
Chris Clifton, Murat Kantarcioglu and Jaideep Vaidya
Scientific Data Mining
15 Mining Temporally-Varying Phenomena in Scientific Datasets
Raghu Machiraju, Srinivasan Parthasarathy, John Wilkins, David S. Thompson, Boyd Gatlin, David Richie, Tat-Sang S. Choy, Ming Jiang, Sameep Mehta, Matthew Coatney, Stephen A. Barr and Kaden Hazzard
16 Methods for Mining Protein Contact Maps
Mohammed J. Zaki, Jingjing Hu and Chris Bystroff
17 Mining Scientific Data Sets Using Graphs
Michihiro Kuramochi, Mukund Deshpande and George Karypis
18 Challenges in Environmental Data Warehousing and Mining
Nabil R. Adam, Vijayalakshmi Atluri, Dihua Guo and Songmei Yu
19 Trends in Spatial Data Mining
Shashi Shekhar, Pusheng Zhang, Yan Huang and Ranga Raju Vatsavai
20 Challenges in Scientific Data Mining: Heterogenous, Biased, and Large Samples
Zoran Obradovic and Slobodan Vucetic
Web, Semantics, and Data Mining
21 Web Mining -- Concepts, Applications, and Research Directions
Jaideep Srivastava, Prasanna Desikan and Vipin Kumar
22 Advancements in Text Mining Algorithms and Software
Svetlana Y. Mironova, Michael W. Berry, Scott Atchley and Micah Beck
23 On Data Mining, Semantics, and Intrusion Detection, What to Dig for and Where to Find It
Anupam Joshi and Jeffrey L. Undercoffer
24 Usage Mining for and on the Semantic Web
Bettina Berendt, Gerd Stumme and Andreas Hotho
Bibliography 481
Index 533