Active mining in a distributed setting

Srinivasan Parthasarathy, Sandhya Dwarkadas, Mitsunori Ogihara

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


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 publicationLarge-Scale Parallel Data Mining
EditorsChing-Tien Ho, Mohammed J. Zaki
PublisherSpringer Verlag
Number of pages18
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)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999
Country/TerritoryUnited States
CitySan Diego

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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