A Novel Approach for Association Mining Based on Matrix Factorization and Deep Neural Network

Harvendra Kumar and Dr. Rakesh Kumar Yadav

Association Rule Mining (ARM) is used for distinguishing proof of relationship between substantial arrangements of information things. Because of vast amount of information put away in databases, a few businesses are getting to be worried in mining affiliation rules from their databases. This paper present a viable instance of applying market basket analysis on a certifiable deals exchange informational collection utilizing time arrangement grouping, as opposed to utilizing customary affiliation manage mining. Association Rule Mining is used for discovering interesting patterns from a large database. Because of vast amount of information available in databases, it is difficult to extract useful information. The proposed grouping process finds numerous arrangements of reciprocal parts, where each arrangement of parts are utilized to make a similar item. Such data is helpful for strategically pitching and estimating. This paper presents a Deep Neural Network based approach for Association Rule Mining (ARM). The analysis of proposed framework suggests that the usage Deep Neural Network with matrix factorization will help in mining association rules that are normally invisible.

Volume 11 | 04-Special Issue

Pages: 1781-1787