Abstract
Zero-suppressed decision diagrams (ZDDs) are a data structure for representing combinations over item sets. They have been applied to many areas such as data mining. When ZDDs represent large-scale sparse datasets, they tend to obtain an unbalanced form, which results performance degradation. In this paper, we propose a new data structure three-way indexing ZDD, as a variant of ZDDs. We furthermore present algorithms to convert between three-way indexing ZDDs and ordinary ZDDs. Experimental results show the effectiveness of our data structure and algorithms.
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A frequent itemset with support threshold \(n\) is an itemset \(X\) such that the number of rows containing all items in \(X\) is more than or equal to \(n\). A frequent itemset is maximal if it is not contained in any other frequent itemset with the same support threshold.
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Aoki, H., Toda, T., Minato, Si. (2014). Three-way Indexing ZDDs for Large-Scale Sparse Datasets. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_41
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DOI: https://doi.org/10.1007/978-3-319-13186-3_41
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