Data Mining: Next Generation Challenges and Future Directions (Paperback) (資料探勘:下一代挑戰與未來方向)
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: 9.8 折 $1,294
- 語言: 英文
- 頁數: 528
- 裝訂: Paperback
- ISBN: 0262612038
- ISBN-13: 9780262612036
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相關分類:
Data-mining
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商品描述
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 Sivakumar3 2 Research Issues in Mining and Monitoring of Intelligence Data
Alan Demers, Johannes Gehrke and Mirek Riedewald27 3 A Consensus Framework for Integrating Distributed Clusterings under Limited Knowledge Sharing
Joydeep Ghosh, Alexander Strehl and Srujana Merugu47 4 Design of Distributed Data Mining Applications on the Knowledge Grid
Mario Cannataro, Domenico Talia and Paolo Trunfio67 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 Weinberger89 6 Mining Frequent Patterns in Data Streams at Multiple Time Granularities
Chris Giannella, Jiawei Han, Jian Pei, Xifeng Yan and Philip S. Yu105 7 Efficient Data-Reduction Methods for On-Line Association Rule Discovery
Hervé Brönnimann, Bin Chen, Manoranjan Dash, Peter Haas and Peter Scheuermann125 8 Discovering Recurrent Events in Multichannel Data Streams Using Unsupervised Methods
Milind R. Naphade, Chung-Sheng Li and Thomas S. Huang147 Counterterrorism, Privacy, and Data Mining 9 Data Mining for Counterterrorism
Bhavani Thuraisingham157 10 Biosurveillance and Outbreak Detection
Paola Sebastiani and Kenneth D. Mandl185 11 MINDS -- Minnesota Intrusion Detection System
Levent Ertöz, Eric Eilertson, Aleksandar Lazarevic, Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava and Paul Dokas199 12 Marshalling Evidence through Data Mining in Support of Counter Terrorism
Daniel Barbará, James J. Nolan, David Schum and Arun Sood219 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 Costa239 14 Defining Privacy for Data Mining
Chris Clifton, Murat Kantarcioglu and Jaideep Vaidya255 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 Hazzard273 16 Methods for Mining Protein Contact Maps
Mohammed J. Zaki, Jingjing Hu and Chris Bystroff291 17 Mining Scientific Data Sets Using Graphs
Michihiro Kuramochi, Mukund Deshpande and George Karypis315 18 Challenges in Environmental Data Warehousing and Mining
Nabil R. Adam, Vijayalakshmi Atluri, Dihua Guo and Songmei Yu335 19 Trends in Spatial Data Mining
Shashi Shekhar, Pusheng Zhang, Yan Huang and Ranga Raju Vatsavai357 20 Challenges in Scientific Data Mining: Heterogenous, Biased, and Large Samples
Zoran Obradovic and Slobodan Vucetic381 Web, Semantics, and Data Mining 21 Web Mining -- Concepts, Applications, and Research Directions
Jaideep Srivastava, Prasanna Desikan and Vipin Kumar405 22 Advancements in Text Mining Algorithms and Software
Svetlana Y. Mironova, Michael W. Berry, Scott Atchley and Micah Beck425 23 On Data Mining, Semantics, and Intrusion Detection, What to Dig for and Where to Find It
Anupam Joshi and Jeffrey L. Undercoffer437 24 Usage Mining for and on the Semantic Web
Bettina Berendt, Gerd Stumme and Andreas Hotho461 Bibliography 481 Index 533
商品描述(中文翻譯)
描述:
資料探勘或稱知識發現,已成為許多領域的企業和研究人員不可或缺的技術。資料探勘借鑒統計學、機器學習、模式識別、數據庫和高性能計算等領域的工作,從現在可用於工業和科學的大型數據集中提取有用信息。本書收集了該領域最新的進展,並為未來的研究方向提供了指引。
第一部分探討普遍存在、分散和流式資料探勘,討論的主題包括新應用領域的分散資料探勘算法、下一代資料探勘系統和應用的幾個方面,以及數字媒體中重複模式的檢測。第二部分考慮了資料探勘、反恐和隱私問題,研究了生物監測、通過資料探勘收集證據以及鏈接發現等主題。第三部分探討科學資料探勘,主題包括挖掘時間變化現象、使用圖形的數據集以及空間資料探勘。最後一部分考慮了網絡、語義和資料探勘,研究了文本探勘算法和軟件、語義網絡等主題。
目錄:
前言
序言
普遍存在、分散和流式資料探勘
1. 分散資料探勘的存在樂趣
科學資料探勘
網絡、語義和資料探勘