Fuzzy approaches to context variable in fuzzy geographically weighted clustering
This work addresses a specific computational challenge in geo-demographic data analysis, representing an incremental improvement to an existing method.
The paper tackles the difficulty of determining exact values for context variables in Fuzzy Geographically Weighted Clustering by proposing two novel fuzzy approaches, with a numerical example provided to illustrate their use.
Fuzzy Geographically Weighted Clustering (FGWC) is considered as a suitable tool for the analysis of geo-demographic data that assists the provision and planning of products and services to local people. Context variables were attached to FGWC in order to accelerate the computing speed of the algorithm and to focus the results on the domain of interests. Nonetheless, the determination of exact, crisp values of the context variable is a hard task. In this paper, we propose two novel methods using fuzzy approaches for that determination. A numerical example is given to illustrate the uses of the proposed methods.