Abstract: Mining interesting itemsets from transaction database has attracted a lot of research work for more than a decade.However,most of these studies either use frequency/support(e.g.,frequent itemset mining) or utility/profit (e.g.,high utility itemset mining) as the key interestingness measure.In other words,these two measures are consi-dered individually,which leads to some shortages that frequent itemsets may have low profit,or high profit itemsets may have very low support,so it is meaningless to recommend these itemsets to users.To this end,we considered these two measures from a unified perspective.Specifically,we proposed to identify the qualified itemsets which are both frequent and high utility.The key challenge to these problems is that the value of utility does not change monotonically when we add more items to a given itemset.Thus,we proposed an efficient algorithm named FHIMA (Frequent and High utility Itemset Mining Algorithms),where an effective upper bound based on frequency and utility is designed to further prune the search space.Moreover,FHIMA incorporates the idea of Prefixspan to avoid generating candidates,thus leading to high efficiency.Finally,the experiment results demonstrate the efficiency of FHIMA on real and synthetic datasets.

Key words: Top-k,Frequent,High utility,Qualified itemsets