Data Mining: Next Generation Challenges and Future Directions (Paperback) (資料探勘：下一代挑戰與未來方向)

Description:

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	3
2	Research Issues in Mining and Monitoring of Intelligence Data Alan Demers, Johannes Gehrke and Mirek Riedewald	27
3	A Consensus Framework for Integrating Distributed Clusterings under Limited Knowledge Sharing Joydeep Ghosh, Alexander Strehl and Srujana Merugu	47
4	Design of Distributed Data Mining Applications on the Knowledge Grid Mario Cannataro, Domenico Talia and Paolo Trunfio	67
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	89
6	Mining Frequent Patterns in Data Streams at Multiple Time Granularities Chris Giannella, Jiawei Han, Jian Pei, Xifeng Yan and Philip S. Yu	105
7	Efficient Data-Reduction Methods for On-Line Association Rule Discovery Hervé Brönnimann, Bin Chen, Manoranjan Dash, Peter Haas and Peter Scheuermann	125
8	Discovering Recurrent Events in Multichannel Data Streams Using Unsupervised Methods Milind R. Naphade, Chung-Sheng Li and Thomas S. Huang	147
	Counterterrorism, Privacy, and Data Mining
9	Data Mining for Counterterrorism Bhavani Thuraisingham	157
10	Biosurveillance and Outbreak Detection Paola Sebastiani and Kenneth D. Mandl	185
11	MINDS -- Minnesota Intrusion Detection System Levent Ertöz, Eric Eilertson, Aleksandar Lazarevic, Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava and Paul Dokas	199
12	Marshalling Evidence through Data Mining in Support of Counter Terrorism Daniel Barbará, James J. Nolan, David Schum and Arun Sood	219
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	239
14	Defining Privacy for Data Mining Chris Clifton, Murat Kantarcioglu and Jaideep Vaidya	255
	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	273
16	Methods for Mining Protein Contact Maps Mohammed J. Zaki, Jingjing Hu and Chris Bystroff	291
17	Mining Scientific Data Sets Using Graphs Michihiro Kuramochi, Mukund Deshpande and George Karypis	315
18	Challenges in Environmental Data Warehousing and Mining Nabil R. Adam, Vijayalakshmi Atluri, Dihua Guo and Songmei Yu	335
19	Trends in Spatial Data Mining Shashi Shekhar, Pusheng Zhang, Yan Huang and Ranga Raju Vatsavai	357
20	Challenges in Scientific Data Mining: Heterogenous, Biased, and Large Samples Zoran Obradovic and Slobodan Vucetic	381
	Web, Semantics, and Data Mining
21	Web Mining -- Concepts, Applications, and Research Directions Jaideep Srivastava, Prasanna Desikan and Vipin Kumar	405
22	Advancements in Text Mining Algorithms and Software Svetlana Y. Mironova, Michael W. Berry, Scott Atchley and Micah Beck	425
23	On Data Mining, Semantics, and Intrusion Detection, What to Dig for and Where to Find It Anupam Joshi and Jeffrey L. Undercoffer	437
24	Usage Mining for and on the Semantic Web Bettina Berendt, Gerd Stumme and Andreas Hotho	461
	Bibliography	481
	Index	533