Mining fuzzy frequent trends from time series

Authors:
Chun-Hao Chen;Tzung-Pei Hong;Vincent S. Tseng
Affiliations:
Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan 701, Taiwan, ROC;Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan, ROC;Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan 701, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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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 need domain knowledge 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. approach to mine fuzzy frequent trends from time series. It uses fuzzy concepts to deal with the value-boundary problem and is less domain-dependent as Udechukwu's approach was. The proposed approach first transform 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 fuzzy frequent trends. Appropriate post-processing is also performed to remove redundant patterns. Finally, experiments are also made for different parameter settings, with experimental results showing that the proposed algorithm can actually work.