期刊
COMPUTERS IN INDUSTRY
卷 53, 期 3, 页码 345-364出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.compind.2003.10.006
关键词
process mining; knowledge discovery; data mining; temporal patterns; association rules; sequential patterns
Existing work in process mining focuses on the discovery of the underlying process model from their instances. In this paper, we do not assume the existence of a single process model to which all process instances comply, and the goal is to discover a set of frequently occurring temporal patterns. Discovery of temporal patterns can be applied to various application domains to support crucial business decision-making. In this study, we formally defined the temporal pattern discovery problem, and developed and evaluated three different temporal pattern discovery algorithms, namely TP-Graph, TP-Itemset and TP-Sequence. Their relative performances are reported. (C) 2003 Elsevier B.V. All rights reserved.
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