A New Homogeneity Inter-Clusters Measure in SemiSupervised Clustering

TL;DR

This paper introduces a novel homogeneity measure for semi-supervised clustering that improves accuracy by enhancing similarity computation, demonstrating significant experimental improvements over existing methods.

Contribution

It proposes a new homogeneity inter-clusters measure specifically designed for semi-supervised clustering, advancing the accuracy of clustering results.

Findings

Significantly improved clustering accuracy with the new measure

Enhanced similarity computation leads to better cluster quality

Experimental results validate the effectiveness of the proposed approach

Abstract

Many studies in data mining have proposed a new learning called semi-Supervised. Such type of learning combines unlabeled and labeled data which are hard to obtain. However, in unsupervised methods, the only unlabeled data are used. The problem of significance and the effectiveness of semi-supervised clustering results is becoming of main importance. This paper pursues the thesis that muchgreater accuracy can be achieved in such clustering by improving the similarity computing. Hence, we introduce a new approach of semisupervised clustering using an innovative new homogeneity measure of generated clusters. Our experimental results demonstrate significantly improved accuracy as a result.