A Hash based Mining Algorithm for Maximal Frequent Item Sets using Linear Probing

Data mining is having a vital role in many of the applications like market-basket analysis, in biotechnologyfield etc. In data mining, frequent itemsets plays an important role which is used to identify thecorrelations among the fields of database. In this paper, we propose an algorithm, HBMFI-LP which hashingtechnology to store the database in vertical data format. To avoid hash collisions, linear probing technique isutilized. The proposed algorithm generates the exact set of maximal frequent itemsets directly by removing all nonmaximalitemsets. The proposed algorithm is compared with the recently developed MAFIA algorithm and is shownthat the HBMFI-LP outperforms in the order of two to three