Authors: Barrientos, Zacarias Jr. Duruin, Delvo, Kirstin Pearl Lopez, and Tuliao, Jelica Aubrey Samson

Issue: 2020

Abstract:

Market Basket Analysis is a mathematical modeling technique based upon the theory that if you buy a certain group of items, you are likely to buy another group of
items. It is used to analyze the customer purchasing behavior and helps in increasing the sales and maintain inventory. There are several methods of performing this analysis, Association analysis being one of them. In Association analysis some rules are framed depending upon the type of business and data is mined for meaning. Most people are aware of the concepts of a shopping cart from buying products online, and it can be extremely useful to track the contents of these baskets. Each basket can provide valuable insight into customer buying patterns and provide the opportunity to identify options for cross selling. To analyze the behavior, the customer purchase information is needed. The association rules are then applied to this information to get the end result. At the completion of this activity we will be able to analyze the customer-purchasing behavior. The primary purpose of this study is to know the capabilities of Market Basket Analysis that poses problems that could puzzle the minds of those in the business industry. The proponent found that the Apriori algorithm has two fatal deficiencies: more scans for the databases and generates a great deal of candidate itemsets. To solve these two deficiencies of Apriori algorithm, an efficient and fast algorithm is proposed, which solves these two problems: scanning for the databases is cut down to one time and not generating the candidate itemsets but generating the frequent itemsets directly. It greatly reduces the temporal complexity and spatial complexity of the algorithm and promotes the efficiency of Apriori algorithm.