Market Basket Data-Mining Analysis

Abstract

The Market Basket analysis is the key factor for customer-centric marketing in this era. It strongly requires the data mining techniques on massive sales transaction data. The aim in this paper is to study and find the different data mining solutions for large and sparse sales transaction data. Here a real-world data set has been summarized and analyzed. In this paper the problems related to Association rule mining (ARM) on large and sparse data has been discussed. It has also shown that the application of association rule mining on sparse data is not easy if implemented directly, so there is a significant need to find some other mining technique and solutions like k-means clustering in this paper, to pre-process the data for ARM. Recency, Frequency and Monetary (RFM) model has been discussed and implemented in detail, so that K-Means algorithm can be applied easily. Additionally, this analysis will be helpful in the future research horizons like multi label classification of temporal data set and sequence to sequence neural network implementation for prediction.