Analyzing time-series data by fuzzy data-mining technique

Chun Hao Chen*, Tzung Pei Hong, S. Tseng

*Corresponding author for this work

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

4 Scopus citations

Abstract

Time series analysis has always been an important and interesting research field due to its frequent appearance in different applications. In this paper, we attempt to use the data mining technique to analyze time series. Many previous studies on data mining have focused on handling binary-valued data. Time series data, however, are usually quantitative values. We thus extend our previous fuzzy mining approach for handling time-series data to find linguistic association rules. The proposed approach first uses a sliding window to generate continues subsequences from a given time series and then analyzes the fuzzy itemsets from these subsequences. Appropriate post-processing is then performed to remove redundant patterns. Experiments are also made to show the performance of the proposed mining algorithm. Since the final results are represented by linguistic rules, they will be friendlier to human than quantitative representation.

Original languageEnglish
Title of host publication2005 IEEE International Conference on Granular Computing
Pages112-117
Number of pages6
DOIs
StatePublished - 1 Dec 2005
Event2005 IEEE International Conference on Granular Computing - Beijing, China
Duration: 25 Jul 200527 Jul 2005

Publication series

Name2005 IEEE International Conference on Granular Computing
Volume2005

Conference

Conference2005 IEEE International Conference on Granular Computing
CountryChina
CityBeijing
Period25/07/0527/07/05

Keywords

  • Association rule
  • Data mining
  • Fuzzy set
  • Time series

Fingerprint Dive into the research topics of 'Analyzing time-series data by fuzzy data-mining technique'. Together they form a unique fingerprint.

Cite this