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Efficiently mining cohesion-based patterns and rules in event sequences

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Discovering patterns in long event sequences is an important data mining task. Traditionally, research focused on frequency-based quality measures that allow algorithms to use the anti-monotonicity property to prune the search space and efficiently discover the most frequent patterns. In this work, we step away from such measures, and evaluate patterns using cohesion — a measure of how close to each other the items making up the pattern appear in the sequence on average. We tackle the fact that cohesion is not an anti-monotonic measure by developing an upper bound on cohesion in order to prune the search space. By doing so, we are able to efficiently unearth rare, but strongly cohesive, patterns that existing methods often fail to discover. Furthermore, having found the occurrences of cohesive itemsets in the input sequence, we use them to discover the representative sequential patterns and the dominant partially ordered episodes, without going through the computationally expensive candidate generation procedures typically associated with sequential pattern and episode mining. Experiments show that our method efficiently discovers important patterns that existing state-of-the-art methods fail to discover.

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  1. The algorithm was given no name by its authors.

  2. The implementations of Winepi, Laxman and \({\textsc {Marbles}}_{\textsc {w}}\) are available at

  3. The implementation of CMW was kindly provided by the author, but is not publicly available.

  4. The implementation of the generator is available at

  5. The implementation of \({\textsc {FCI}}_{\textsc {SEQ}}\) is available at




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The authors would like to thank the VLAIO SBO HYMOP project for funding this research.

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Correspondence to Len Feremans.

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Responsible editor: Mohammed J. Zaki.

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A preliminary version appeared as “Efficient Discovery of Sets of Co-occurring Items in Event Sequences“ (Cule et al. 2016). Sections 2.4 and 3.6 are based on “Mining Association Rules in Long Sequences” (Cule and Goethals 2010).



Top-25 patterns

Tables 11 and 12 show the top-25 itemsets for Species and Trump, Tables 13 and 14 show the top-25 sequential patterns, and Tables 15 and 16 the top-25 association rules. Note that, as discussed in Sect. 4, \({\textsc {FCI}}_{\textsc {seq}}\) produced fewer than 25 sequential patterns per dataset, due to the usage of the minimal occurrence ratio threshold. A lower threshold would naturally result in more patterns, but we argue that these patterns are better omitted from the output, since they are in fact not representative of the occurrences of the underlying itemset. Patterns for \({\textsc {FCI}}_{\textsc {seq}}\) in bold are not reported by any other state-of-the-art method in the top-1000, likewise, patterns in bold for other methods are not reported by \({\textsc {FCI}}_{\textsc {seq}}\) in the top-1000. Note that since we only produce fewer than 25 sequential patterns, nearly all of the patterns found by other methods are in bold.

Table 11 Top 25 itemsets for Species
Table 12 Top 25 itemsets for Trump
Table 13 Top 25 sequential patterns for Species
Table 14 Top 25 sequential patterns for Trump
Table 15 Top 25 rules for Species
Table 16 Top 25 rules for Trump

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Cule, B., Feremans, L. & Goethals, B. Efficiently mining cohesion-based patterns and rules in event sequences. Data Min Knowl Disc 33, 1125–1182 (2019).

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