IMCS: Incremental Mining of Closed Sequential Patterns
Recently, mining compact frequent patterns (for example closed patterns and compressed patterns) has received much attention from data mining researchers. These studies try to address the interpretability and efficiency problems encountered by traditional frequent pattern mining methods. However, to the best of our knowledge, how to efficiently mine compact sequential patterns in a dynamic sequence database environment has not been explored yet.
In this paper, we examine the problem how to mine closed sequential patterns incrementally. A compact structure CSTree is designed to keep the closed sequential patterns, and an efficient algorithm IMCS is developed to maintain the CSTree when the sequence database is updated incrementally. A thorough experimental study shows that IMCS outperforms the state-of-the-art algorithms – PrefixSpan, CloSpan, BIDE and a recently proposed incremental mining algorithm IncSpan by about a factor of 4 to more than an order of magnitude.
KeywordsSequential Pattern Frequent Pattern Hash Table Pattern Mining Sequential Pattern Mining
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