A Clustering Method Based on Information Entropy Payload

TL;DR

This paper proposes an entropy-based clustering method that maximizes average information entropy within clusters, eliminating the need for preset parameters like the number of clusters, and enhancing information expression efficiency.

Contribution

It introduces a novel clustering approach based on information entropy that does not require preset parameters and improves information expression in results.

Findings

Does not require preset parameters like number of clusters

Achieves maximum average information entropy in clusters

Applicable to image segmentation and object classification

Abstract

Existing clustering algorithms such as K-means often need to preset parameters such as the number of categories K, and such parameters may lead to the failure to output objective and consistent clustering results. This paper introduces a clustering method based on the information theory, by which clusters in the clustering result have maximum average information entropy (called entropy payload in this paper). This method can bring the following benefits: firstly, this method does not need to preset any super parameter such as category number or other similar thresholds, secondly, the clustering results have the maximum information expression efficiency. it can be used in image segmentation, object classification, etc., and could be the basis of unsupervised learning.