Analysis of Subsequence Time-Series Clustering Based on Moving Average

Subsequence time-series clustering (STSC), which consists of subsequence cutout with a sliding window and k-means clustering, had been commonly used in time-series data mining. However, a problem was pointed out that STSC always generates moderate sinusoidal patterns independently of the input. To address this problem, we theoretically explain and empirically confirm the similarity between STSC and moving average. The present analysis is consistent with, and simpler than, one of the most important analyses of STSC. We also question the pattern extraction in the time domain and discuss another solution.

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