Active mining in a distributed setting

Srinivasan Parthasarathy, Sandhya Dwarkadas, Mitsunori Ogihara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Most current work in data mining assumes that the data is static, and a database update requires re-mining both the old and new data. In this article, we propose an alternative approach. We outline a general strategy by which data mining algorithms can be made active — i.e., maintain valid mined information in the presence of user interaction and database updates. We describe a runtime framework that allows efficient caching and sharing of data among clients and servers. We then demonstrate how existing algorithms for four key mining tasks: Discretization, Association Mining, Sequence Mining, and Similarity Discovery, can be re-architected so that they maintain valid mined information across i) database updates, and ii) user interactions in a client-server setting, while minimizing the amount of data re-accessed.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages65-82
Number of pages18
Volume1759
ISBN (Print)3540671943, 9783540671947
StatePublished - 2002
Externally publishedYes
Event5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999 - San Diego, United States
Duration: Aug 15 1999Aug 15 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1759
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999
CountryUnited States
CitySan Diego
Period8/15/998/15/99

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Parthasarathy, S., Dwarkadas, S., & Ogihara, M. (2002). Active mining in a distributed setting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1759, pp. 65-82). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1759). Springer Verlag.