A Clustering Method Based on Information Entropy Payload
This addresses the issue of parameter dependency in clustering for applications like image segmentation and object classification, though it appears incremental as it builds on existing information theory concepts.
The paper tackles the problem of clustering algorithms requiring preset parameters like the number of categories, which can lead to inconsistent results, by introducing a method based on information theory that maximizes average information entropy, eliminating the need for such parameters and enhancing information expression efficiency.
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.