Fuzzy information granules in time series data

Often, it is desirable to represent a set of time series through typical shapes in order to detect common patterns. The algorithm presented here compares pieces of a different time series in order to find such similar shapes. The use of a fuzzy clustering technique based on fuzzy c-means allows us to detect shapes that belong to a certain group of typical shapes with a degree of membership. Modifications to the original algorithm also allow this matching to be invariant with respect to a scaling of the time series. The algorithm is demonstrated on a widely known set of data taken from the electrocardiogram (ECG) rhythm analysis experiments performed at the Massachusetts Institute of Technology (MIT) laboratories and on data from protein mass spectrography. (C) 2004 Wiley Periodicals, Inc.