Abstract:
Market Basket Analysis discovers consumer purchasing patterns by identifying important and
interesting association rules among the products bought in a shopping basket. This field of
study is not only useful in the decision-making process, but also to increase the sales and profit
of an organization. Association rule mining is one of the prime data mining techniques. Among
the algorithms studied under association rule mining, ‘Apriori’, ‘ECLAT’ and ‘FP-Growth’ are
key algorithms applied in diversified fields. Although an enormous number of studies have
been conducted in association rule mining in market basket analysis, it is still a budding area
in Sri Lanka, especially in the field of retailing of small and medium enterprises. The research
is based on grocery transaction data obtained from a local mini-supermarket during a fourmonth time period, to discover important information and interesting relationships to aid
decision making to enhance performance and to gain a competitive advantage. The goals of the
study are: to identify frequently consumed items and to generate association rules to understand
the purchasing patterns of the customer to make recommendations for cross-selling and upselling of products using the dataset; to use three algorithms of association rule mining and
compare their performance using different datasets with varying characteristics of inputs which
reflect the small and medium retail industry. The findings listed the most frequently purchased
items and brands along with the items that were bought together most of the time, leading to
many recommendations and planning strategies. In addition to these findings, the performance
evaluation of the association rule mining algorithm findings showed that the ‘Apriori’ performs
best in terms of execution time, even with a higher number of candidate generation at low
minimum support values, compared to the other two algorithms ‘ECLAT’ and ‘FP-Growth’.
