Meta-clustering of possibilistically segmented retail datasets

Abstract

This paper proposes a possibilistic meta-clustering algorithm. The possibility theory is used for possibilistic segmentation of the input data as well as for determining the possibilistic membership of objects to multiple clusters. The meta-clustering uses connections between information granules to send clustering knowledge from one granule to another. The approach is demonstrated with the help of the k-modes clustering algorithm for a real-world retail store, where a customer is connected to the products bought, and products are connected to customers who buy them. The meta-clustering approach uses the results from clustering of customers as an input to cluster the products, and recursively uses clustering of products as input to the clustering customers. The customer granule is represented using static information from the database and dynamic information from the clustering of the products bought. Similarly, a product granule is represented by static information from the database and dynamic part from the clustering of the customers who buy the product. The static information from the database is represented using possibilistic segments.