Clustering of Customers from Massive Customer Transaction Data

Neethu CM, Anitha Abraham, Linda Sebastian

Abstract: In this internet era, more and more people use online shopping. Analysing massive customer transaction data about these online activities can be used to improve the business and to satisfy customer demands in a better way. In this research paper we try to study different methods employed to analyse the customer transaction data. In our study we have studied methods like K-Means clustering, PAM clustering, Agglomerative, Divisive and Density Based clustering methods. Based on our study we have identified that K-Means is the widely used clustering method.

Keywords: Clustering, Partitional clustering, Hierarchical Clustering