A less domain-dependent fuzzy mining algorithm for frequent trends

C. H. Chen*, T. P. Hong, S. Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Time series analysis has always been an important and interesting research field due to its frequent appearance in different applications. In the past, many mining approaches were proposed to find useful patterns from time-series data. Time-series data, however, are usually quantitative values and domain knowledge is needed to predefine crisp intervals of categories for a mining process to proceed. In this paper, we thus propose an algorithm based on Udechukwu et al's approach to mine fuzzy frequent trends from time series without referring to domain knowledge. The proposed approach first transforms data values into angles, and then uses a sliding window to generate continues subsequences from angular series. The Apriori-like fuzzy mining algorithm is then used to generate frequent trends. Appropriate post-processing is also performed to remove redundant patterns. Finally, experiments are also made for different parameter settings.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Fuzzy Systems
Pages851-856
Number of pages6
DOIs
StatePublished - 1 Dec 2006
Event2006 IEEE International Conference on Fuzzy Systems - Vancouver, BC, Canada
Duration: 16 Jul 200621 Jul 2006

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2006 IEEE International Conference on Fuzzy Systems
CountryCanada
CityVancouver, BC
Period16/07/0621/07/06

Keywords

  • Data mining fuzzy set
  • Fuzzy frequent trends
  • Time series

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    Chen, C. H., Hong, T. P., & Tseng, S. (2006). A less domain-dependent fuzzy mining algorithm for frequent trends. In 2006 IEEE International Conference on Fuzzy Systems (pp. 851-856). [1681810] (IEEE International Conference on Fuzzy Systems). https://doi.org/10.1109/FUZZY.2006.1681810