Preventing meaningless stock time series pattern discovery by changing perceptually important point detection

Tak Chung Fu, Fu Lai Korris Chung, Wing Pong Robert Luk, Chak Man Ng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Discovery of interesting or frequently appearing time series patterns is one of the important tasks in various time series data mining applications. However, recent research criticized that discovering subsequence patterns in time series using clustering approaches is meaningless. It is due to the presence of trivial matched subsequences in the formation of the time series subsequences using sliding window method. The objective of this paper is to propose a threshold-free approach to improve the method for segmenting long stock time series into subsequences using sliding window. The proposed approach filters the trivial matched subsequences by changing Perceptually Important Point (PIP) detection and reduced the dimension by PIP identification.
Original languageEnglish
Title of host publicationFuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings
PublisherSpringer Verlag
Number of pages4
ISBN (Print)9783540283126
Publication statusPublished - 1 Jan 2006
Event2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsa, China
Duration: 27 Aug 200529 Aug 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3613 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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