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

The problem of miningfrequent weighted itemsets(FWIs) is an extension of the miningfrequent 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 miningfrequent 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. Theweighted 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.

Automated summary

The study introduces an efficient algorithm NFWCI for mining frequent weighted closed itemsets (FWCIs) using the WN-list structure and early pruning strategy, showing superior performance compared to existing algorithms in experimental results.