An Alternate Approach to Improve Access Time for Defining Frequent Item Set Through ‘A-Apriori’ in Textual Data Set
Neeraj Kumar Verma, Sandeep Kumar, Mukesh Kumar, Praful Saxena · 2020
Today so many data mining algorithms exist for classification through association rules, among them the most popular classification algorithm is the Apriori algorithm. Basically Apriori algorithm is used to define the frequent itemsets from big transactional data set by scanning the whole database looking for k-element frequent item set. As per the Apriori algorithm here we analyze and going to reduce access time which consumes in scanning the database for k-times looking for k-element frequent item set. In this paper, we are going to analyze and compare our proposed Ameliorated-Apriori Algorithm (A-Apriori) with original Apriori algorithm which concludes the experimental result to calculate frequent items on several groups of transactions with minimum support (for both Apriori & A-Apriori) and improve its performance by reducing the time consumption in accessing the database by 67%. Our proposed A-Apriori algorithm is an Improved version of Apriori algoritam[4] and working much better at every parameter which we include in concluding the results.