Optimization Of The Fuzzy C-Means Cluster Center For Credit Data Grouping Using Genetic Algorithms - Apdilah, Dicky (Al'Adzkiya, 2021)
Data grouping can be used in the marketing strategy of a product. The process of grouping data that previously behaved differently into groups that now behave more uniformly. As with the grouping of creditworthiness assessment data, this data grouping is needed to obtain the dominant values that will characterize each group or segment. The clustering method is quite widely used to overcome problems related to data segmentation. Clustering is a grouping method based on a measure of proximity, the more accurately the clusters are formed, the clearer the similarity of customer behavior patterns will be. Thus, companies can determine marketing strategies more precisely, based on customer behavior patterns. One of the clustering methods that can be used to group data is Fuzzy C-Means (FCM), which is a method of grouping data determined by the degree of membership. Optimization by presenting a Genetic Algorithm to obtain test data cluster results regarding the grouping of credit data. The purpose of this study is to examine the application of the Genetic Algorithm in fuzzy clustering, especially Fuzzy C-Means, and to examine the extent to which the Genetic Algorithm can improve the performance of Fuzzy C-Means in optimizing cluster centers to obtain grouping of customer data which will later be used for assessing creditworthiness.