Abstract

Data mining, as an active field, discovers useful knowledge from large data sets. This paper focuses on continuous time series data that have often been encountered in real applications (e.g., sales records, economic data and stock transactions) and discusses how to discover the hidden relationship among time series patterns in terms of their similarities. Fuzzy clustering and dynamic time warping (DTW) methods are used to deal with fuzzy groupings of data attributes as well as with degrees of distance between time series patterned attributes, respectively. An economic time series example is provided to help illustrate the ideas.

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