Frequent Itemset Mining is one of the most well known techniques to extract knowledge from data. The Apriori algorithm is a classical algorithm for the frequent itemset mining problem. A significant bottleneck in Apriori is the number of I/O operation involved, and the number of candidates it generates. In our work, we investigate the role of LSH techniques to overcome these problems, without adding much computational overhead. We propose randomized variations of Apriori that are based on asymmetric LSH defined over Jaccard similarity and Hamming distance.
Announcements from Dept.
Mar 18, 2017 : Comprehensive Examination - Revised Registration Forms
Jan 10, 2017 : IITM Summer Fellowship Programme.
Dec 16, 2016 : The Semi-annual Progress Report for MS/PhD Scholars is due by Dec.