Evaluation of Class Decomposition based on Clustering Validity and K-means Algorithm

Abstract

A class decomposition is one of the possible solutions and the most important factors of success for the improvement of classification performance. The idea is to transform a dataset by categorizing each class label into groups or clusters. Thus, the transformation is done concerning data characteristics and similarities. This paper proposed a hybrid model for a class decomposition by the integration of gap statistic, k-means clustering algorithm, and Naive Bayes classifier. The model is based on clustering validity using gap statistic for enhancing the classifier performance. The model works by dividing each dataset into several subsets regarding its class labels. After that, the clustering validity using gap statistic is employed for estimating the optimal number of clusters for each subset that belong to a particular class label. The estimated number of clusters is used then as an input parameter for the k-means clustering algorithm for relabeling the data objects with a new class label in each subset. Every data object is allocated to each of the clusters generated by the k-means clustering algorithm, which consider it as the new class label. The proposed model integrates the class decomposition approach with Naive Bayes classifier to compare the performance of the proposed model under several classification measures. The model is validated and evaluated by employing different real-world datasets collected from the UCI machine learning repository. The experimental results show that a significant improvement in classification accuracy and F-measure when the class decomposition is applied. Also, the experiments indicate that using a class decomposition is not appropriate for all datasets.