Abstract
Discriminative pattern mining is known under the names of subgroup discovery, contrast set mining, emerging pattern mining, etc. and has been intensively studied for the last 15 years. Based on the sophisticated techniques developed so far (e.g. branch-and-bound search, minimum support raising, and redundancy elimination including the use of closed patterns), this paper proposes an efficient exact algorithm for finding top-k discriminative patterns that are not redundant and would be of value at a later step in prediction or knowledge discovery. The proposed algorithm is unique in that it conducts depth-first search over enumeration trees in a mirrored form of conventional ones, and by this design we can keep compact the list of candidate top-k patterns during the search and consequently high the minimum support threshold. Experimental results with the datasets from UCI Machine Learning Repository clearly show the efficiency of the proposed algorithm.
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Kameya, Y., Asaoka, H. (2013). Depth-First Traversal over a Mirrored Space for Non-redundant Discriminative Itemsets. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2013. Lecture Notes in Computer Science, vol 8057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40131-2_17
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DOI: https://doi.org/10.1007/978-3-642-40131-2_17
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