Frequent item set mining is used that focuses on find out recurrent correlations in the data. Change mining, it focuses on frequent itemsets, focuses on important changes in the set of mined itemsets from one point in time period to another. The finding of frequent generalized itemsets, One dynamic pattern, the history generalized pattern ,that represents the development of an itemset in successive time periods, by accounting the information about its recurrent generalizations characterized by minimal redundancy some time it becomes infrequent. Higen mining, The higen miner, that focuses on avoiding itemset mining followed by postprocessing by developing a support-driven itemset generalization .To focus the attention on the minimally redundant recurrent generalizations and reduce the amount of the generated patterns, the finding a subset of higens, namely the nonredundant higens,. Tests do on both real and synthetic datasets show the competence and the effectiveness [1] .