FINDING PATTERN BEHAVIOR IN TEMPORAL DATA USING FUZZY CLUSTERING
Richard E. Haskell, Darrin M. Hanna, P Li, Ka C. Cheok · 2000
A clustering technique based on a fuzzy equivalence relation is used to characterize temporal data. Data collected during an initial time period are separated into clusters. These clusters are characterized by their centroids. Clusters formed during subsequent time periods are either merged with an existing cluster or added to the cluster list. The resulting list of cluster centroids, called a cluster group, characterizes the behavior of a particular set of temporal data. The degree to which new clusters formed in a subsequent time period are similar to the cluster group is characterized by a similarity measure, q. This technique has been applied to the problem of detecting driver behavior.