Enhancing clustering quality of geo-demographic analysis using context fuzzy clustering type-2 and particle swarm optimization

• We focused on improving clustering quality of Geo-Demographic Analysis.
• A novel interval type-2 fuzzy clustering algorithm was presented.
• It included some additional techniques to speed up the whole algorithm.
• The proposed method obtained better clustering quality than relevant algorithms.
• Suitable parameters for the best quality of the proposed algorithm were discussed.

Geo-Demographic Analysis, which is one of the most interesting inter-disciplinary research topics between Geographic Information Systems and Data Mining, plays a very important role in policies decision, population migration and services distribution. Among some soft computing methods used for this problem, clustering is the most popular one because it has many advantages in comparison with the rests such as the fast processing time, the quality of results and the used memory space. Nonetheless, the state-of-the-art clustering algorithm namely FGWC has low clustering quality since it was constructed on the basis of traditional fuzzy sets. In this paper, we will present a novel interval type-2 fuzzy clustering algorithm deployed in an extension of the traditional fuzzy sets namely Interval Type-2 Fuzzy Sets to enhance the clustering quality of FGWC. Some additional techniques such as the interval context variable, Particle Swarm Optimization and the parallel computing are attached to speed up the algorithm. The experimental evaluation through various case studies shows that the proposed method obtains better clustering quality than some best-known ones.

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