Knowledge Discovery from Series of Interval Events
Journal of Intelligent Information Systems - Data warehousing and knowledge discovery
Maintaining knowledge about temporal intervals
Communications of the ACM
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Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Temporal patterns composed of symbolic intervals are commonly formulated with Allen's interval relations originating in temporal reasoning. This representation has severe disadvantages for knowledge discovery. The Time Series Knowledge Representation (TSKR) is a new hierarchical language for interval patterns expressing the temporal concepts of coincidence and partial order. We present effective and efficient mining algorithms for such patterns based on itemset techniques. A novel form of search space pruning effectively reduces the size of the mining result to ease interpretation and speed up the algorithms. On a real data set a concise set of TSKR patterns can explain the underlying temporal phenomena, whereas the patterns found with Allen's relations are far more numerous yet only explain fragments of the data.