@article{EzennayaGomez2018Minin-45355,
  year={2018},
  doi={10.2991/ijcis.11.1.39},
  title={Mining Frequent Synchronous Patterns based on Item Cover Similarity},
  number={1},
  volume={11},
  issn={1875-6891},
  journal={International Journal of Computational Intelligence Systems},
  pages={525--539},
  author={Ezennaya-Gomez, Salatiel and Borgelt, Christian}
}

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    <dc:creator>Borgelt, Christian</dc:creator>
    <dc:contributor>Ezennaya-Gomez, Salatiel</dc:contributor>
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    <dcterms:title>Mining Frequent Synchronous Patterns based on Item Cover Similarity</dcterms:title>
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    <dc:creator>Ezennaya-Gomez, Salatiel</dc:creator>
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    <dcterms:abstract xml:lang="eng">In previous work we presented CoCoNAD (Continuous-time Closed Neuron Assembly Detection), a method to find significant synchronous patterns in parallel point processes with the goal to analyze parallel neural spike trains in neurobiology. A drawback of CoCoNAD and its accompanying methodology of pattern spectrum filtering (PSF) and pattern set reduction (PSR) is that it judges the (statistical) significance of a pattern only by the number of synchronous occurrences (support). However, the same number of occurrences can be significant for patterns consisting of items with a generally low occurrence rate, but explainable as a chance event for patterns consisting of items with a generally high occurrence rate, simply because more item occurrences produce more chance coincidences of items. In order to amend this drawback, we present in this paper an extension of the recently introduced CoCoNAD variant that is based on influence map overlap support (which takes both the number of synchronous events and the precision of synchrony into account), namely by transferring the idea of Jaccard item set mining to this setting: by basing pattern spectrum filtering upon item cover similarity measures, the number of coincidences is related to the item occurrence frequencies, which leads to an improved sensitivity for detecting synchronous events (or parallel episodes) in sequence data. We demonstrate the improved performance of our method by extensive experiments on artificial data sets.</dcterms:abstract>
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    <dc:contributor>Borgelt, Christian</dc:contributor>
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    <dc:rights>Attribution-NonCommercial 4.0 International</dc:rights>
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