Mining top-k regular-frequent itemsets using database partitioning and support estimation

Temporal regularity of itemset appearance can be regarded as an important criterion for measuring the interestingness of itemsets in several applications. A frequent itemset can be said to be regular-frequent in a database if it appears at a regular period. Therefore, the problem of mining a complete set of regular-frequent itemsets requires the specification of a support and a regularity threshold. However, in practice, it is often difficult for users to provide an appropriate support threshold. In addition, the use of a support threshold tends to produce a large number of regular-frequent itemsets and it might be better to ask for the number of desired results. We thus propose an efficient algorithm for mining top-k regular-frequent itemsets without setting a support threshold. Based on database partitioning and support estimation techniques, the proposed algorithm also uses a best-first search strategy with only one database scan. We then compare our algorithm with the state-of-the-art algorithms for mining top-k regular-frequent itemsets. Our experimental studies on both synthetic and real data show that our proposal achieves high performance for small and large values of k.     

Erratum to: “Communication Complexity and Intrinsic Universality in Cellular Automata” [Theor. Comput. Sci. 412 (1-2) (2011) 2-21]

Proofs of Propositions 6 and 8 of the paper Communication Complexity and Intrinsic Universality in Cellular Automata are formally incorrect. This erratum proves weaker versions of Propositions 6 and 8 and a stronger version of Proposition 9 which are sufficient to get the main results of the paper (Corollary 2) for PREDICTION and INVASION problems. For problem CYCLE, we only prove a weaker version of Corollary 2, essentially replacing a condition of the form ‘f∈Ω(n)’ by ‘f∉o(n)’. All other statements of the paper are unaffected.