Intelligent Geodemographic Clustering Based on Neural Network and Particle Swarm Optimization

Abstract

Most of the techniques involved in customer clustering and segmentation are based on conventional methods of quantitative analysis or traditional data mining approaches such as the K-Means algorithm. However, clustering approaches based on artificial neural networks (ANNs), evolutionary algorithms, and fuzzy methods can be more efficient since they can reveal nonlinear patterns. They also seem to be more robust in coping with noise-related issues and relevant noise handling operations. They do not make any statistical distributional assumptions regarding the nature of the data. In this article, we develop a hybrid approach based on ANNs and swarm intelligence to reveal the underlying pattern structure of customers of an insurance company in the Republic of Ireland. This model is tailored to the scope of segmenting administrative districts, or “small areas,” given policyholders’ spatial characteristics. To that end, the geospatial features of customers are taken into account. Geodemographically speaking, by implementing such a hybrid model, the relative similarity among spatial objects (small areas in this work) are preserved. In this way, the similarity of each small area to all other small areas is characterized. Consequently, the pattern of customers is analyzed using an optimal and intelligent solution. We can also visualize the results of this study.