A Dissimilarity Measure for Comparing Subsets of Data : Application to Multivariate Time Series ∗

Similarity is a central concept in data mining. Many techniques, such as clustering and classification, use similarity or distance measures to compare various subsets of multivariate data. However, most of these measures are only designed to find the distances between a pair of records or attributes in a data set, and not for comparing whole data sets against one another. In this paper we present a novel dissimilarity measure based on principal component analysis for doing such comparisons between such data sets, and in particular time series data sets. Our measure accounts for the correlation structure of the data, and can be tuned by the user to account for domain knowledge. Our measure is useful in such applications as change point detection, anomaly detection, and clustering in fields such as intrusion detection, clinical trial data analysis, and stock analysis.

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