The Algorithm Expansion for Starting Point Determination Using Clustering Algorithm Method with Fuzzy C-Means
Edrian Hadinata, Rahmat Widia Sembiring, Tien Fabrianti Kusumasari, Tutut Herawan · Advances in intelligent systems and computing · 2016
The starting point determination in Fuzzy C-Means algorithm (FCM) is taken by random. Thus, the algorithm for starting point determination was developed with Hierarchical Agglomerative Clustering approach as a substitution of membership degree randomization process in the early iteration. It is expected that the clustering process will produce fewer iteration. The process contained on this algorithm is the incorporation of a number of clusters based on the approach contained in complete linkage. Then it will calculate the difference in the objective function for each iterations after the clustering process has been conducted on the FCM. The iteration process will be stopped after the difference of objective function is smaller than the prescribed limit. In this research, analysis of variance from the obtained cluster produces a good homogeneity and heterogeneity value. In addition, the number of iteration is getting fewer.