Mining Non-redundant Information-Theoretic Dependencies between Itemsets

We present an information-theoretic framework for mining dependencies between itemsets in binary data. The problem of closure-based redundancy in this context is theoretically investigated, and we present both lossless and lossy pruning techniques. An efficient and scalable algorithm is proposed, which exploits the inclusion-exclusion principle for fast entropy computation. This algorithm is empirically evaluated through experiments on synthetic and real-world data.

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