叠加信息熵游走数据聚类算法

在数据聚类的过程中，由于样本数据空间分布的复杂性，相似度度量过程中的重复性以及算法的自适应性等问题，聚类算法往往无法得到正确的聚类结果．为了解决样本数据空间分布复杂的问题，提出叠加信息熵数据游走聚类算法．该算法通过在数值空间构建样本叠加信息熵场，并通过数据游走进行数据分割实现聚类．实验结果表明，该算法不仅可以获得较好的聚类效果，同时具有较高的数据自适应性．

Clustering by samples movement in the superposition information entropy field

The essence of clustering is to compare the similarities among samples on different scales. Due to the complexity of the spatial distribution of sample data, the similarity measurement in the process of repeatability, and the algorithm for adaptive problems, the clustering algorithm cannot lead to the correct result in the process of data clustering. In order to solve the complex problem of spatial distribution of sample data, we present a data migration clustering algorithm based on superposition information entropy, which is used to construct the entropy field of the data in the numerical space, and the datawandering to implement data segmentation and complete clustering. Experimental results show that this method can not only obtain a better clustering effect, but also have data adaptability.