Abstract. Many business and scientific domains require the collection and analysis of sequences of events and time series data. Although statistical approaches have been long applied to time series, most of these approaches assume the time series is stationary and typically must be applied globally to the sequence. Thus, other methods are needed to solve many types of problems that occur in sequential business and scientific applications. One such problem is when there is no global correlation between sequences, but there are periodic occurrences when the signature of one sequence is present in other sequences. This can be solved by using association rules that relate the sequences of events, as described in this dissertation. Discovering association rules relating the sequences of events is an important data mining problem first addressed by Das et al. in [16]. In this dissertation, we build on their work by using representative association rules [44], closures [77], and constraints to drive our association analysis over sequences of events. Representative association rules together with closures have not been applied to event sequences, as performed in this dissertation. As in [16], we first discretize and cluster the data into sequences of events. These sequences of events
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