Survey on Clustering of Massive Customer Transaction Data
Mrs. Sonali L. Mortale,  Mrs. Manisha Darak
Today a clustering of customer transaction data is very important procedure and to analyze customer behaviors in retail and e-commerce companies. Product from companies is organized as product tree, in which the leaf nodes are goods to sell, and the internal nodes (except root node) could be multiple product categories. We propose the “personalized product tree”, named purchase tree, to represent a customer’s transaction records. Customer’s transaction data set can be compressed into a set of purchase trees. We also propose a partitioned clustering algorithm, named PurTreeClust, for fast clustering of purchase trees. To cluster the purchase tree data, we first rank the purchase trees as candidate representative trees with a novel separate density, and then select the top k customers as the representatives of k customer groups. We also propose a gap statistic based method to evaluate the number of clusters. We use C 4.5 algorithm for making a decision tree, which can show different transaction of customer to make better purchase decision. Finally, the clustering results are obtained by assigning each customer to the nearest representative.
Keywords- Customer segmentation, clustering transaction data, purchase tree, clustering trees.
Cite this Article
Mrs. Sonali L. Mortale,  Mrs. Manisha Darak,   "Survey on Clustering of Massive Customer Transaction Data"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.6, Issue 4, pp.419-421, December 2018, Available at :http://www.ijedr.org/papers/IJEDR1804077.pdf