1 research outputs found
Performance Comparison of Association Rule Algorithms with SPMF on Automotive Industry Data
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