叠加信息熵游走数据聚类算法

doi:10.3969/j.issn.1001-2400.2018.04.014

Abstract

Abstract:

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.

Key words: custering, information entropy field, samples movement

XU Tuo;LI Jie;WANG Ying. Clustering by samples movement in the superposition information entropy field[J].Journal of Xidian University, 2018, 45(4): 75-79+154.

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Clustering by samples movement in the superposition information entropy field

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