Optimization Model of K-Means Clustering Using Artificial Neural Networks to Handle Class Imbalance Problem

Abstract

Class imbalance is a situation where instances in one class much higher than instances in other classes. In clustering, this problem not only affects the accuracy of a prediction but also introduces bias in decision-making process. In this case, a machine learning technique will yield a good prediction accuracy from training data class with a large number of instances, but give a poor accuracy in classes with the small number of instances. In this research, we propose an approach for optimizing K-Means clustering in handling class imbalance problem. The approach uses the perceptron feed-forward neural network to determine coordinates of the centroid of a cluster in K-Means clustering processes. Data used in this research are datasets from the UCI Machine Learning Repository. From the experimental results obtained, the proposed approach could optimize the result of K-Means clustering in terms of minimizing class imbalance.