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

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

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內容描述

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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
MiningHillol Kargupta and Krishnamoorthy
Sivakumar
3

2
Research Issues in Mining and Monitoring of
Intelligence DataAlan Demers, Johannes Gehrke and Mirek
Riedewald
27

3
A Consensus Framework for Integrating Distributed
Clusterings under Limited Knowledge SharingJoydeep Ghosh,
Alexander Strehl and Srujana Merugu
47

4
Design of Distributed Data Mining Applications on the
Knowledge GridMario Cannataro, Domenico Talia and Paolo
Trunfio
67

5
Photonic Data Services: Integrating Data, Network and
Path Services to Support Next Generation Data Mining
ApplicationsRobert 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 GranularitiesChris Giannella, Jiawei Han, Jian Pei,
Xifeng Yan and Philip S. Yu
105

7
Efficient Data-Reduction Methods for On-Line
Association Rule DiscoveryHervé Brönnimann, Bin Chen,
Manoranjan Dash, Peter Haas and Peter Scheuermann
125

8
Discovering Recurrent Events in Multichannel Data
Streams Using Unsupervised MethodsMilind R. Naphade,
Chung-Sheng Li and Thomas S. Huang
147

Counterterrorism, Privacy, and Data Mining

9
Data Mining for CounterterrorismBhavani
Thuraisingham
157

10
Biosurveillance and Outbreak
DetectionPaola Sebastiani and Kenneth D.
Mandl
185

11
MINDS -- Minnesota Intrusion Detection
SystemLevent 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 TerrorismDaniel Barbará, James J. Nolan, David
Schum and Arun Sood
219

13
Relational Data Mining with Inductive Logic
Programming for Link DiscoveryRaymond 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 MiningChris
Clifton, Murat Kantarcioglu and Jaideep Vaidya
255

Scientific Data Mining

15
Mining Temporally-Varying Phenomena in Scientific
DatasetsRaghu 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
MapsMohammed J. Zaki, Jingjing Hu and Chris
Bystroff
291

17
Mining Scientific Data Sets Using
GraphsMichihiro Kuramochi, Mukund Deshpande and George
Karypis
315

18
Challenges in Environmental Data Warehousing and
MiningNabil R. Adam, Vijayalakshmi Atluri, Dihua Guo and
Songmei Yu
335

19
Trends in Spatial Data MiningShashi
Shekhar, Pusheng Zhang, Yan Huang and Ranga Raju
Vatsavai
357

20
Challenges in Scientific Data Mining: Heterogenous,
Biased, and Large SamplesZoran Obradovic and Slobodan
Vucetic
381

Web, Semantics, and Data Mining

21
Web Mining -- Concepts, Applications, and Research
DirectionsJaideep Srivastava, Prasanna Desikan and Vipin
Kumar
405

22
Advancements in Text Mining Algorithms and
SoftwareSvetlana 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 ItAnupam Joshi and Jeffrey
L. Undercoffer
437

24
Usage Mining for and on the Semantic
WebBettina Berendt, Gerd Stumme and Andreas
Hotho
461

Bibliography
481

Index
533

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