Mining temporal patterns from sequence database of interval-based events

Yen Liang Chen, Shin Yi Wu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Sequential pattern mining is one of the important techniques of data mining to discover some potential useful knowledge from large databases. However, existing approaches for mining sequential patterns are designed for point-based events. In many applications, the essence of events are interval-based, such as disease suffered, stock price increase or decrease, chatting etc. This paper presents a new algorithm to discover temporal pattern from temporal sequences database consisting of interval-based events.

Original languageEnglish
Title of host publicationFuzzy Systems and Knowledge Discovery - Third International Conference, FSKD 2006, Proceedings
PublisherSpringer Verlag
Pages586-595
Number of pages10
ISBN (Print)3540459162, 9783540459163
DOIs
StatePublished - 2006
Event3rd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2006 - Xi'an, China
Duration: 24 Sep 200628 Sep 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4223 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2006
Country/TerritoryChina
CityXi'an
Period24/09/0628/09/06

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