Incmarfi: Mining Maximal Regular Frequent Itemsets in Incremental Databases
نویسندگان
چکیده
In incremental databases, new transactions will add continuously to the old transactions. Maximal frequent patterns are one of the condensed representations of frequent patterns. Recently, regular pattern mining along with frequent patterns playing an important role in data mining research. Several algorithms have been proposed so far to mine maximal frequent patterns on various domains. There is no suitable algorithm to mine maximal regular frequent (MRF) itemsets in incremental databases using transaction_ids. So, in this paper we are proposing a new algorithm called IncMaRFI to mine MRF itesmsets in incremental databases using common items from a set of transaction_id pairs. Our algorithm extracts all the latest MRF itemset(s) at a time with in a single scan. Our experiment results show the out performance of our algorithm.
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