Clustering of Information Granules in Hotspot Identification

Conceptually and algorithmically, hotspots could be regarded as information granules. In this study, we propose an aggregation of Fuzzy C-Means (FCM) algorithm and the principle of justifiable granularity (PJG) as a new approach to forming hotspots. With the proposed method, the quality of the hotspots formed in this manner could also be provided as an additional information to the decision makers. Moreover, a weighted granular clustering method is presented to further abstract the constructed hotspots, and this delivers a higher level of abstraction of the phenomenon of interest. A collection of synthetic data is used to show the proposed process of identifying the hotspots, and to demonstrate its differences with some other representative hotspot identification methods. Besides, real-world data are also used to illustrate the performance of the proposed method.