• A.KANIMOZHI
• MS.C.GRACE PADMA

ABSTRACT The emergence of the business-to-customer (B2C) markets has resulted in various studies on developing and improving customer retention and profit enhancement. This is mainly due to the retail business becoming increasingly competitive with costs being driven down by new and existing competitors. In general, consumer markets have several characteristics such as repeat buying over the relevant time interval, a large number of customers, and a wealth of information detailing past customer purchases The provision of customized service to the customers is vital for a company to establish long lasting and pleasant relationship with consumers. It has also been observed that keeping old customers generates more profit than attracting new ones. So, customer retention is a big factor too. So, there is always a trade-off between customer benefits and transaction costs, which has to be optimized by the managers. The purpose of this thesis is to study, implement and analyze various Data-mining tools and techniques and then do an analysis of the sample / raw data to obtain a meaningful interpretation. Some of the data mining algorithms I have used, are a vector quantization based clustering algorithm, and then an Apriori based Association rule mining algorithm. The first one is aimed at a meaningful segregation of the various customers based on their RFM values, while the latter algorithm tries to find out relationships and patterns among the purchases made by the customer, over several transactions.

"Personalized market basket prediction with temporal annotated recurring sequences", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.120-143, June 2019, Available :http://www.jetir.org/papers/JETIR1907U56.pdf

"Personalized market basket prediction with temporal annotated recurring sequences", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp120-143, June 2019, Available at : http://www.jetir.org/papers/JETIR1907U56.pdf