Mining Segment-Wise Periodic Patterns in Time-Related Databases

Jiawei Han, Wan Gong, Yiwen Yin

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

Abstract

Periodicity search, that is, search for cychcity in time-related databases, is an interesting data mining problem. Most previous studies have been on finding full-cycle periodicity for aü the segments in the selected sequences of the data, that is, if a sequence is periodic, all the points or segments in the period repeat. However, it is often useful to mine segment-wise or point-wise periodicity in (ime-related data sets. In this study, we integrate data cube and Apriori data mining techniques for mining segment-wise periodicity in regard to a fixed length period and show that data cube provides an efficient structure and a convenient way for interactive mining of multiple-level periodicity.

Original languageEnglish (US)
Title of host publicationProceedings of the 4th International Conference on Knowledge Discovery and Data Mining, KDD 1998
PublisherAmerican Association for Artificial Intelligence (AAAI) Press
Pages214-218
Number of pages5
ISBN (Electronic)1577350707, 9781577350705
StatePublished - 1998
Externally publishedYes
Event4th International Conference on Knowledge Discovery and Data Mining, KDD 1998 - New York City, United States
Duration: Aug 27 1998Aug 31 1998

Publication series

NameProceedings of the 4th International Conference on Knowledge Discovery and Data Mining, KDD 1998

Conference

Conference4th International Conference on Knowledge Discovery and Data Mining, KDD 1998
Country/TerritoryUnited States
CityNew York City
Period8/27/988/31/98

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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