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Title
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Mining top-k quantile-based cohesive sequential patterns
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Author
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Abstract
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| Finding patterns in long event sequences is an important data mining task. Two decades ago research focused on finding all frequent patterns, where the anti-monotonic property of support was used to design efficient algorithms. Recent research focuses on producing a smaller output containing only the most interesting patterns. To achieve this goal, we introduce a new interestingness measure by computing the proportion of the occurrences of a pattern that are cohesive. This measure is robust to outliers, and is applicable to sequential patterns. We implement an efficient algorithm based on constrained prefix-projected pattern growth and pruning based on an upper bound to uncover the set of top-k quantile-based cohesive sequential patterns. We run experiments to compare our method with existing state-of-the-art methods for sequential pattern mining and show that our algorithm is efficient and produces qualitatively interesting patterns on large event sequences. |
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Language
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English
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Source (book)
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Proceedings of the 2018 SIAM International Conference on Data Mining, May 3-5, 2018, San Diego, CA, USA
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Publication
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San Diego, Calif.
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SIAM
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2018
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ISBN
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978-1-61197-532-1
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Volume/pages
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p. 90-98
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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