Stock time series categorization and clustering Via SB-tree optimization

Tak Chung Fu, Chi Wai Law, Kin Kee Chan, Fu Lai Korris Chung, Chak Man Ng

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

5 Citations (Scopus)


SB-Tree is a data structure proposed to represent time series according to the importance of the data points. Its advantages over traditional time series representation approaches include: representing time series directly in time domain (shape preservation), retrieving time series data according to the importance of the data points and facilitating multi-resolution time series retrieval. Based on these benefits, one may find this representation particularly attractive in financial time series domain and the corresponding data mining tasks, i.e. categorization and clustering. In this paper, an investigation on the size of the SB-Tree is reported. Two SB-Tree optimization approaches are proposed to reduce the size of the SB-Tree while the overall shape of the time series can be preserved. As demonstrated by various experiments, the proposed approach is suitable for different categorization and clustering applications.
Original languageEnglish
Title of host publicationFuzzy Systems and Knowledge Discovery - Third International Conference, FSKD 2006, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540459162, 9783540459163
Publication statusPublished - 1 Jan 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


Conference3rd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2006

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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