Fuzziness Evaluation on Hybrid Context Based Clustering Methods with Fuzzy Geographically Weighted Clustering-Particle Swarm Optimization Algorithm

Abstract

This research contains to hybrid Context Based Clustering Method integrated with Fuzzy Geographically Weighted Clustering-Particle Swarm Optimization (CFGWC-PSO). The computation time using CFGWC-PSO Algorithm is faster than other algorithms. CFGWC-PSO algorithm was applied on 11 variables from data factors causing the spread of dengue in East Java. One of the parameters used in this analysis is fuzziness (m), which is the parameter used to measure the level of obscurity from the clustering results. In this paper will use different fuzziness (m) values to evaluating best fuzziness value (m) which are appropriate used to clustering with CFGWC-PSO algorithm. CFGWC-PSO algorithm using fuzziness (m) = 1.5 and fuzziness (m) = 2, number of clusters = 2 then CFGWC-PSO will evaluated using IFV index. Based on IFV index found that the best clustering in this case with CFGWC-PSO algorithm is with using fuzziness value (m) = 2.