Performance Comparison of Association Rule Algorithms with SPMF on Automotive Industry Data

Abstract

By the recent developments about the information technologies, companies can store their data faster and easier with lower costs. All transactions (sales, current card, invoicing, etc.) performed in companies during the day combine at the end of the day to form big datasets. It is possible to extract valuable information through these datasets with data mining. And this has become more important for companies in terms of today's conditions where the competition in the market is high. In this study, a dataset of a company selling car maintenance and repair products in Turkey is used. Association Rules are applied on this dataset for determining the items which are bought together by the customers. These rules, which are calculated specifically for the company, can be used to redefine the sales and marketing strategies, to revise the storage areas efficiently, and to create sales campaigns suitable for the customers and regions. These algorithms are also called Frequent Itemset Mining Algorithms. The most recent 11 algorithms from these are applied to this dataset in order to compare the performances according to metrics like memory usage and execution times against varying support values and varying record numbers by using SPMF platform. Three different datasets are created by using the whole dataset like 6-months, 12-months and 22-months. According to the experiments, it can be said that executon times generally increases inversely with the support values as nearly all algorithms have higher execution time values for the lowest support value of 0.1. dEclat_bitset algorithm has the most efficient performance for 6-months and 12-months dataset. But Eclat algorithm can be said to be the most efficient algorithm for 0.7 and 0.3 support values; on the other hand dEclat_bitset is the most efficient algorithm for 0.3 and 0.1 support values on 22-months dataset.