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- Title
MARKET BASKET ANALYSIS USING FP GROWTH AND APRIORI ALGORITHM: A CASE STUDY OF MUMBAI RETAIL STORE.
- Authors
Venkatachari, Kavitha; Chandrasekaran, Issac Davanbu
- Abstract
Companies nowadays are rich in vast amounts of data but poor in information extracted from that data. Big data is seen as a valuable resource and although the concept of data mining is still new and developing, companies in a variety of industries are relying on it for making strategic decisions. Facts that otherwise may go unnoticed can be now revealed by the techniques that sift through stored information. Market basket analysis is a very useful technique for finding out co-occurring items in consumer shopping baskets. Such information can be used as a basis for decisions about marketing activity such as promotional support, inventory control and cross-sale campaigns. The main objective of the research paper is to see how different products in a grocery store assortment interrelate and how to exploit these relations by marketing activities. Mining association rules from transactional data will provide us with valuable information about co-occurrences and co-purchases of products. Such information can be used as a basis for decisions about marketing activity such as promotional support, inventory control and cross-sale campaigns. To find association rules we use two algorithms.(FP growth and Apriori algorithm).To find out frequent item sets using R tool and Rapid miner. As per the paper FP growth is much slow in Rapid Miner and in R Programming Apriori algorithm is fast. We have collected data from Mumbai Retail Store and the sample size for the analysis is 300.
- Subjects
MUMBAI (India); MARKET basket analysis; APRIORI algorithm; ASSOCIATION rule mining; RETAIL stores
- Publication
BVIMSR Journal of Management Research, 2016, Vol 8, Issue 1, p56
- ISSN
0976-4739
- Publication type
Article