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Spectral clustering algorithm has been demonstrated to be an effective unsupervised learning method. The spectral graph theory indicates that the eigenvalues and eigenvectors of the graph Laplacian are closely related with the clustering results. In this paper we prove that the distribution of the eigenvalues describes the distinctness of clusters and the eigenvectors implicitly present the target...
Association analysis arises in many important applications such as bioinformatics and business intelligence. Given a large collection of measurements over a set of samples, association analysis aims to find dependencies of target variables to subsets of measurements. Most previous algorithms adopt a two-stage approach; they first group samples based on the similarity in the subset of measurements,...
Simultaneously clustering columns and rows (co- clustering) of large data matrix is an important problem with wide applications, such as document mining, microarray analysis, and recommendation systems. Several co-clustering algorithms have been shown effective in discovering hidden clustering structures in the data matrix. For a data matrix of m rows and n columns, the time complexity of these methods...
This paper is to investigate a new unsupervised approach for the extracted objects based on synthetic aperture radar (SAR) image using improving fuzzy clustering method. The traditional fuzzy c-means clustering (FCM) is very sensitive to the initial value and the number of clusters. The accurate initial value and number of clusters are important parameters to get the accurate result in FCM. SAR image...
FCM algorithm is a well-known unsupervised clustering algorithm, which needs to know the number of clusters in advance. This paper proposes a method that uses GA according to the optimal value of an evaluation index CS to determine the best cluster result and its corresponding clustering number of FCM. This method prevents from the subjective and large computation by traditional method. The simulation...
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