A Less Domain-dependent Fuzzy Mining Algorithm for Frequent Trends

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 a priori-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.