Fuzzy data mining for time-series data

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


Time series analysis has always been an important and interesting research field due to its frequent appearance in different applications. In the past, many approaches based on regression, neural networks and other mathematical models were proposed to analyze the time series. 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
Pages (from-to)536-542
Number of pages7
JournalApplied Soft Computing Journal
Issue number1
StatePublished - 1 Jan 2012


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

Fingerprint Dive into the research topics of 'Fuzzy data mining for time-series data'. Together they form a unique fingerprint.

Cite this