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Efficient mining of all margin-closed itemsets with applications in temporal knowledge discovery and classification by compression

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Abstract

Margin-closed itemsets have previously been proposed as a subset of the closed itemsets with a minimum margin constraint on the difference in support to supersets. The constraint reduces redundancy in the set of reported patterns favoring longer, more specific patterns. A variety of patterns ranging from rare specific itemsets to frequent general itemsets is reported to support exploratory data analysis and understandable classification models. We present DCI_Margin, a new efficient algorithm that mines the complete set of margin-closed itemsets. We modified the DCI_Closed algorithm that has low memory requirements and can be parallelized. The margin constraint is checked on-the-fly reusing information already computed by DCI_Closed. We thoroughly analyzed the behavior on many datasets and show how other data mining algorithms can benefit from the redundancy reduction.

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Authors and Affiliations

  1. Siemens Corporate Research, 755 College Road East, Princeton, NJ, 08540, USA

    Fabian Moerchen

  2. Databionic Research Group, Philipps-University Marburg, Hans-Meerwein-Str, 35032, Marburg, Germany

    Michael Thies & Alfred Ultsch

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  1. Fabian Moerchen
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  2. Michael Thies
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  3. Alfred Ultsch
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Correspondence to Fabian Moerchen.

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Moerchen, F., Thies, M. & Ultsch, A. Efficient mining of all margin-closed itemsets with applications in temporal knowledge discovery and classification by compression. Knowl Inf Syst 29, 55–80 (2011). https://doi.org/10.1007/s10115-010-0329-5

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