Analysis of Data classification accuracy using ANFIS algorithm modification with K-Medoids clustering

Abstract

The ANFIS algorithm is a technique in data mining that can be used for the data classification process. The ANFIS algorithm still has weaknesses, especially in determining the initial parameters for the network training process. Thus, an additional algorithm or modification is needed for the determination of these parameters. In this study, a clustering method will be proposed, namely K-Medoids Clustering as an additional method to the ANFIS algorithm. Basically, the ANFIS algorithm uses the FCM (Fuzzy C-Means Clustering) algorithm for the initial initialization of network parameters. The use of this method can cause local minima problems, where the clustering results obtained are not optimal because the pseudo-partition matrix generation process is carried out randomly. The matrix value will determine the initial parameter value in the ANFIS algorithm used in the first layer. Based on the research that has been done, it can be concluded that the accuracy of data classification using the ANFIS algorithm which has been modified with the proposed method provides a fairly good influence in conducting training and classification testing. The increase that occurs in the proposed method is 0.73% for the average training accuracy and an increase of 0.66% for the average testing accuracy.