Performance Comparison of Association Rule Algorithms with SPMF on Automotive Industry Data - Melih Nair; Fatih Kayaalp (Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 2019)
By the recent developments about theinformation technologies, companies can store their data faster and easier withlower costs. All transactions (sales, current card, invoicing, etc.) performedin 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 datamining. And this has become more important for companies in terms of today'sconditions where the competition in the market is high. In this study, adataset of a company selling car maintenance and repair products in Turkey isused. Association Rules are applied on this dataset for determining the itemswhich are bought together by the customers. These rules, which are calculatedspecifically for the company, can be used to redefine the sales and marketingstrategies, to revise the storage areas efficiently, and to create salescampaigns suitable for the customers and regions. These algorithms are alsocalled Frequent Itemset Mining Algorithms. The most recent 11 algorithms fromthese are applied to this dataset in order to compare the performances accordingto metrics like memory usage and execution times against varying support valuesand varying record numbers by using SPMF platform. Three different datasets arecreated by using the whole dataset like 6-months, 12-months and 22-months. Accordingto the experiments, it can be said that executon times generally increasesinversely with the support values as nearly all algorithms have higherexecution time values for the lowest support value of 0.1. dEclat_bitsetalgorithm 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 and0.3 support values; on the other hand dEclat_bitset is the most efficientalgorithm for 0.3 and 0.1 support values on 22-months dataset.