Large Scale Data Mining: Challenges and Responses

Jaturon Chattratichat; John Darlington; Moustafa Ghanem; Yike Guo; Harald Hüning; Martin Köhler; Janjao Sutiwaraphun; Hing Wing To; Dan Yang

Large Scale Data Mining: Challenges and Responses

Jaturon Chattratichat, John Darlington, Moustafa Ghanem, Yike Guo, Harald Hüning, Martin Köhler, Janjao Sutiwaraphun, Hing Wing To, Dan Yang

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

57 Citations (Scopus)

Abstract

Data mining over large data-sets is important due to its obvious commercial potential. However, it is also a major challenge due to its computational complexity. Exploiting the inherent parallelism of data mining algorithms provides a direct solution by utilising the large data retrieval and processing power of parallel architectures. In this paper, we present some results of our intensive research on parallelising data mining algorithms. In particular, we also present a methodology for determining the proper parallelisatlon strategy based on the idea of algorithmic skeletons and performance modelling. This research aims to provide a systematic way to develop parallel data mining algorithms and applications.

Original language	English
Title of host publication	Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997
Editors	David Heckerman, Heikki Mannila, Daryl Pregibon, Ramasamy Uthurusamy
Publisher	AAAI Press
Pages	143-146
Number of pages	4
ISBN (Electronic)	1577350278, 9781577350279
Publication status	Published - 1997
Externally published	Yes
Event	3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997 - Newport Beach, United States Duration: 14 Aug 1997 → 17 Aug 1997

Publication series

Name	Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997

Conference

Conference	3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997
Country/Territory	United States
City	Newport Beach
Period	14/08/97 → 17/08/97

Bibliographical note

Publisher Copyright:
Copyright © 1997, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.

Cite this

Chattratichat, J., Darlington, J., Ghanem, M., Guo, Y., Hüning, H., Köhler, M., Sutiwaraphun, J., To, H. W., & Yang, D. (1997). Large Scale Data Mining: Challenges and Responses. In D. Heckerman, H. Mannila, D. Pregibon, & R. Uthurusamy (Eds.), Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997 (pp. 143-146). (Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997). AAAI Press.

Chattratichat, Jaturon ; Darlington, John ; Ghanem, Moustafa et al. / Large Scale Data Mining : Challenges and Responses. Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997. editor / David Heckerman ; Heikki Mannila ; Daryl Pregibon ; Ramasamy Uthurusamy. AAAI Press, 1997. pp. 143-146 (Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997).

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title = "Large Scale Data Mining: Challenges and Responses",

abstract = "Data mining over large data-sets is important due to its obvious commercial potential. However, it is also a major challenge due to its computational complexity. Exploiting the inherent parallelism of data mining algorithms provides a direct solution by utilising the large data retrieval and processing power of parallel architectures. In this paper, we present some results of our intensive research on parallelising data mining algorithms. In particular, we also present a methodology for determining the proper parallelisatlon strategy based on the idea of algorithmic skeletons and performance modelling. This research aims to provide a systematic way to develop parallel data mining algorithms and applications.",

author = "Jaturon Chattratichat and John Darlington and Moustafa Ghanem and Yike Guo and Harald H{\"u}ning and Martin K{\"o}hler and Janjao Sutiwaraphun and To, \{Hing Wing\} and Dan Yang",

note = "Publisher Copyright: Copyright {\textcopyright} 1997, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.; 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997 ; Conference date: 14-08-1997 Through 17-08-1997",

year = "1997",

language = "English",

series = "Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997",

publisher = "AAAI Press",

pages = "143--146",

editor = "David Heckerman and Heikki Mannila and Daryl Pregibon and Ramasamy Uthurusamy",

booktitle = "Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997",

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Chattratichat, J, Darlington, J, Ghanem, M, Guo, Y, Hüning, H, Köhler, M, Sutiwaraphun, J, To, HW & Yang, D 1997, Large Scale Data Mining: Challenges and Responses. in D Heckerman, H Mannila, D Pregibon & R Uthurusamy (eds), Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997. Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997, AAAI Press, pp. 143-146, 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997, Newport Beach, United States, 14/08/97.

Large Scale Data Mining: Challenges and Responses. / Chattratichat, Jaturon; Darlington, John; Ghanem, Moustafa et al.
Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997. ed. / David Heckerman; Heikki Mannila; Daryl Pregibon; Ramasamy Uthurusamy. AAAI Press, 1997. p. 143-146 (Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

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AU - Chattratichat, Jaturon

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AU - Ghanem, Moustafa

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AU - Hüning, Harald

AU - Köhler, Martin

AU - Sutiwaraphun, Janjao

AU - To, Hing Wing

AU - Yang, Dan

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AB - Data mining over large data-sets is important due to its obvious commercial potential. However, it is also a major challenge due to its computational complexity. Exploiting the inherent parallelism of data mining algorithms provides a direct solution by utilising the large data retrieval and processing power of parallel architectures. In this paper, we present some results of our intensive research on parallelising data mining algorithms. In particular, we also present a methodology for determining the proper parallelisatlon strategy based on the idea of algorithmic skeletons and performance modelling. This research aims to provide a systematic way to develop parallel data mining algorithms and applications.

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M3 - Conference Paper published in a book

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BT - Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997

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Chattratichat J, Darlington J, Ghanem M, Guo Y, Hüning H, Köhler M et al. Large Scale Data Mining: Challenges and Responses. In Heckerman D, Mannila H, Pregibon D, Uthurusamy R, editors, Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997. AAAI Press. 1997. p. 143-146. (Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997).

Large Scale Data Mining: Challenges and Responses

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