Document Type thesis Author Name Pray, Keith A URN etd-0506104-150831 Title Apriori Sets And Sequences: Mining Association Rules from Time Sequence Attributes Degree MS Department Computer Science Advisors Carolina Ruiz, Advisor Matthew O. Ward, Reader Michael Gennert, Department Head Keywords mining complex data temporal association rules Date of Presentation/Defense 2004-04-29 Availability unrestricted
We introduce an algorithm for mining expressive temporal relationships from complex data. Our algorithm, AprioriSetsAndSequences (ASAS), extends the Apriori algorithm to data sets in which a single data instance may consist of a combination of attribute values that are nominal sequences, time series, sets, and traditional relational values. Datasets of this type occur naturally in many domains including health care, financial analysis, complex system diagnostics, and domains in which multi-sensors are used. AprioriSetsAndSequences identifies predefined events of interest in the sequential data attributes. It then mines for association rules that make explicit all frequent temporal relationships among the occurrences of those events and relationships of those events and other data attributes. Our algorithm inherently handles different levels of time granularity in the same data set. We have implemented AprioriSetsAndSequences within the Weka environment and have applied it to computer performance, stock market, and clinical sleep disorder data. We show that AprioriSetsAndSequences produces rules that express significant temporal relationships that describe patterns of behavior observed in the data set.
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