Online Marketing Frequent Item set Prediction using Eclat Algorithm

Frequent itemset mining is a major field in data mining techniques. This is because it deals with usual and normal occurrences of the set of items in a database transaction. Originated from market basket analysis, frequent itemset generation may lead to the formulation of association rule to derive correlation or patterns. Association rule mining still remains as one of the most prominent areas in data mining that aims to extract interesting correlations, frequent patterns, association or causal structures among a set of items in the transaction databases. The underlying structure of association rules mining algorithms are based upon horizontal or vertical data formats. These two data formats have been widely discussed by showing a few examples of the algorithm of each data formats. The works on horizontal approaches suffer in many candidate generations and multiple database scans that contribute to higher memory consumptions. In response to improving on the horizontal approach, the works on vertical approaches are established. Eclat algorithm is one example of an algorithm in vertical approach database format. Motivated to its "fast intersection", in this paper, we review and analyze the fundamental Eclat and Eclat-variants such as mindset, diffset, and sort different. In response to vertical data format and as continuity to Eclat extension, we propose a post diffuser algorithm as a new member in Eclat variants that use mindset format in the first looping and diffset in the later looping.