Analysis Clustering Using Normalized Cross Correlation In Fuzzy C-Means Clustering Algorithm

Abstract

Abstract: Fuzzy C-Means Clustering (FCM) has been widely known as a technique for performing data clustering, such as image segmentation. This study will conduct a trial using the Normalized Cross Correlation method on the Fuzzy C-Means Clustering algorithm in determining the value of the initial fuzzy pseudo-partition matrix which was previously carried out by a random process. Clustering technique is a process of grouping data which is included in unsupervised learning. Data mining generally has two techniques in performing clustering, namely: hierarchical clustering and partitional clustering. The FCM algorithm has a working principle in grouping data by adding up the level of similarity between pairs of data groups. The method applied to measure the similarity of the data based on the correlation value is the Normalized Cross Correlation (NCC). The methodology in this research is the steps taken to measure clustering performance by adding the Normalized Cross Correlation (NCC) method in determining the initial fuzzy pseudo-partition matrix in the Fuzzy C-Means Clustering (FCM) algorithm. the results of data clustering using the Normalized Cross Correlation (NCC) method on the Fuzzy C-Means Clustering (FCM) algorithm gave better results than the ordinary Fuzzy C-Means Clustering (FCM) algorithm. The increase that occurs in the proposed method is 4.27% for the Accuracy, 4.73% for the rand index and 8.26% for the F-measure..