Clustering Algorithm and Its Application in the Classification of Aurora Based on the Manifold Distance
Abstract: This paper presents a new Spectral clustering analysis algorithm based on the unsupervised learning. Spectral clustering algorithm has its own unique advantage. For example, it can be clustered in any irregular shape of the sample space, but also be obtained the optimal solution in the global. The article prefers to use the clustering algorithm of the similarity measure as the breakthrough point to improve the traditional similarity measure. I use the manifold distance as the similarity measure instead of the Euclidean distance on the basis of the traditional spectral clustering algorithm (NJW-SC). On the basis the object set and the sample clustering can be clus-tered. After I set the experimental comparison with the new algorithm and K-means algorithm, traditional spectral clustering algorithm (NJW-SC), the fuzzy clustering algorithm (FCM) on artificial data set, it can be concluded that the new algorithm has been achieved good results in the convex shape of the data sets and on the global consistency. On UCI data sets, I tried to use the artificial labeling evaluation index F-measure numerical calculation to carry out on the clustering quality. At last, I chose the aurora images and tried to use them to verify that spectral clustering algorithm also had very good application in the aurora classification.
文章引用: 孙羊子 , 王晅 (2016) 基于流行距离的聚类算法及其在极光分类中的应用。 计算机科学与应用， 6， 303-316. doi: 10.12677/CSA.2016.65037
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