A Hash Based Mining Algorithm for Maximal Frequent Item Sets Using Linear Probing
Source: VIT UNIVERSITY
Data mining is having a vital role in many of the applications like market-basket analysis, in biotechnology field etc. In data mining, frequent item-sets plays an important role which is used to identify the correlations among the fields of database. In this paper, the authors propose an algorithm, HBMFI-LP which hashing technology to store the database in vertical data format. To avoid hash collisions, linear probing technique is utilized. The proposed algorithm generates the exact set of maximal frequent item-sets directly by removing all non-maximal item-sets. The proposed algorithm is compared with the recently developed MAFIA algorithm and is shown that the HBMFI-LP outperforms in the order of two to three.