  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.
