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Fast and Memory-Efficient Discovery of the Top-k Relevant Subgroups in a Reduced Candidate Space

Fast and Memory-Efficient Discovery of the Top-k Relevant Subgroups in a Reduced Candidate Space

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. We consider a modified version of the top-k subgroup discovery task, where subgroups dominated by other subgroups are discarded. The advantage of this modified task, known as relevant subgroup discovery, is that it avoids redundancy in the outcome. Although it has been applied in many applications, so far no efficient exact algorithm for this task has been proposed. Most existing solutions do not guarantee the exact solution (as a result of the use of non-admissible heuristics), while the only exact solution relies on the explicit storage of the whole search space, which results in prohibitively large memory requirements. In this paper, we present a new top-k relevant subgroup discovery algorithm which overcomes these shortcomings. Our solution is based on the fact that if an iterative deepening approach is applied, the relevance check - which is the root of the problems of all other approaches - can be realized based solely on the best k subgroups visited so far. The approach also allows for the integration of admissible pruning techniques like optimistic estimate pruning. The result is a fast, memory-efficient algorithm which clearly outperforms existing top-k relevant subgroup discovery approaches. Moreover, we analytically and empirically show that it is competitive with simpler approaches which do not consider the relevance criterion.

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