Discovering Time-Interval Sequential Patterns by a Pattern Growth Approach with Confidence Constraints

[[abstract]]Sequential pattern mining is to discover frequent sequential patterns in a sequence database. The technique is applied to fields such as web click-stream mining, failure forecast, and traffic analysis. Conventional sequential pattern mining approaches generally focus only the orders of items; however, the time interval between two consecutive events can be a more valuable information when the time of the occurrence of an event is concerned. This study extends the concept of the well-known pattern growth approach, PrefixSpan algorithm, to propose a novel sequential pattern mining approach for sequential patterns with time intervals. The current study suggests that the confidence of the occurrence of a pattern is also important other than the frequency (i.e. support) of the pattern. Thus, the proposed approach extracts a pattern by first satisfying a minimum confidence constraint, and then finds out the least time interval that satisfies the minimum support constraint. Experiments are conducted to evaluate the performance of the proposed approach.[[notice]]補正完