Mining approximate frequency itemsets over data streams based on hash and interesting degree
Frequent itemsets mining,which is the basic in the field of data stream mining,has been paid more and more attention by researchers.Due to the uncertainties,continuities and large amount of data streams,many mining algorithms are difficult to deal with these dynamic data streams.In this paper, hashed table and the interesting degree of association rules are introduced,where the former is used to represent the synoptic data structure and the latter is applied to incorporate attention of customers.After that,a new frequent itemsets mining algorithm named MIFS HT(mining interesting frequent itemsets with hash table) is proposed.Comparing with lossy counting and a similar algorithm called mining frequent item sets over data streams by matrix(MISM for short),the result shows that MIFS HT is more effective both in time and space efficiency.