论文标题
基于信息熵有效载荷的聚类方法
A Clustering Method Based on Information Entropy Payload
论文作者
论文摘要
现有的聚类算法（例如K-均值）通常需要预设参数，例如类别K的数量，并且此类参数可能导致未能输出目标和一致的聚类结果。本文介绍了一种基于信息理论的聚类方法，群集结果中的簇具有最大的平均信息熵（在本文中称为熵有效载荷）。此方法可以带来以下好处：首先，此方法不需要预设任何超级参数，例如类别编号或其他类似阈值，其次，聚类结果具有最大的信息表达效率。它可以用于图像分割，对象分类等，并且可以是无监督学习的基础。
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.
