Mining frequent weighted closed itemsets using the WN-list structure and an early pruning strategy

Abstract

The problem of mining frequent weighted itemsets (FWIs) is an extension of the mining frequent itemsets (FIs), which considers not only the frequent occurrence of items but also their relative importance in a dataset. However, like mining FIs, mining FWIs usually produces a large result set, which makes it difficult to extract rules and creates redundancy. The problem of mining frequent weighted closed itemsets (FWCIs) has been proposed as a solution to this issue, which produces a smaller result set while preserving sufficient information to extract rules. The weighted node-list (WN-list) structure is currently considered the state-of-the-art structure for mining FWIs. In this study, we first propose the definition of WN-list ancestral operation and a theorem as the theoretical basis for eliminating unsatisfactory candidates, then propose an efficient algorithm, namely NFWCI, for mining FWCIs using the WN-list and an early pruning strategy. The experimental results on many sparse and dense datasets show that the proposed algorithm outperforms the-state-of-the-art algorithm for mining FWCIs.