A Comparative Study of Pattern Recognition Algorithms on Sales Data

In the realm of Data Mining looking for patterns and association rules is a very critical task and has been widely studied in the past years. There exist several data mining algorithms to find Association Rules in given datasets. One of the most popular and widely used algorithm is the Apriori algorithm to find patterns and itemsets in huge datasets and getting the association rules between them. This is done to gather knowledge from otherwise unsuspecting and random data. The Fp-Growth algorithm is similarly a different algorithm which uses an extended frequent pattern prefix-tree data structure for storing critical data after compression about frequent pairs. In this paper we do a comparative analysis of the 2 most popular pattern recognition algorithms and their performance on sales data of a college canteen sales transnational database where each record consists of items purchased by customer.