Iterative meta-clustering through granular hierarchy of supermarket customers and products

This paper proposes a novel iterative meta-clustering technique that uses clustering results from one set of objects to dynamically change the representation of another set of objects. The proposal evolves two clustering schemes in parallel influencing each other through indirect recursion. The proposal is based on the emerging area of granular computing, where each object is represented as an information granule and an information granule can hierarchically include other information granules. The paper describes the theoretical and algorithmic formulation of the iterative meta-clustering algorithm followed by its implementation. The proposal is demonstrated with the help of a retail store dataset consisting of transactions involving customers and products. A customer granule is represented by static information obtained from the database and dynamic information obtained from clustering of products bought by the customer. Similarly, the product granule augments the static representation from the database with clustering profiles of customers who buy these products. The algorithm is tested for a synthetic dataset to explore various nuances of the proposal, followed by an extensive experimentation with a real-world retail dataset.