Clustering validity based on the most similarity

TL;DR

This paper introduces a new clustering validity measure based on the most similarity, aiming to improve the evaluation of clustering results regardless of input parameters.

Contribution

The paper proposes an efficient clustering validity measure that maximizes the repetition of initial values, addressing limitations of existing density-based methods.

Findings

The new measure effectively evaluates clustering quality.

It reduces dependency on initial parameters.

It performs well on large systems with incremental data.

Abstract

One basic requirement of many studies is the necessity of classifying data. Clustering is a proposed method for summarizing networks. Clustering methods can be divided into two categories named model-based approaches and algorithmic approaches. Since the most of clustering methods depend on their input parameters, it is important to evaluate the result of a clustering algorithm with its different input parameters, to choose the most appropriate one. There are several clustering validity techniques based on inner density and outer density of clusters that represent different metrics to choose the most appropriate clustering independent of the input parameters. According to dependency of previous methods on the input parameters, one challenge in facing with large systems, is to complete data incrementally that effects on the final choice of the most appropriate clustering. Those methods define the existence of high intensity in a cluster, and low intensity among different clusters as the measure of choosing the optimal clustering. This measure has a tremendous problem, not availing all data at the first stage. In this paper, we introduce an efficient measure in which maximum number of repetitions for various initial values occurs.