Abstract
We present a reliable universal method for ranking sequential patterns (itemset-sequences) with respect to significance in the problem of frequent sequential pattern mining. We approach the problem by first building a probabilistic reference model for the collection of itemset-sequences and then deriving an analytical formula for the frequency for sequential patterns in the reference model. We rank sequential patterns by computing the divergence between their actual frequencies and their frequencies in the reference model. We demonstrate the applicability of the presented method for discovering dependencies between streams of news stories in terms of significant sequential patterns, which is an important problem in multi-stream text mining and the topic detection and tracking research.
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Gwadera, R., Crestani, F. (2010). Ranking Sequential Patterns with Respect to Significance. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13657-3_32
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DOI: https://doi.org/10.1007/978-3-642-13657-3_32
Publisher Name: Springer, Berlin, Heidelberg
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