unsupervised clustering algorithm is successfully applied in many fields. While the method of fast search and find of density peaks can efficiently discover the centers of clusters by finding the high-density peaks, i...
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ISBN:
(纸本)9781728111414
unsupervised clustering algorithm is successfully applied in many fields. While the method of fast search and find of density peaks can efficiently discover the centers of clusters by finding the high-density peaks, it suffers from selecting the cluster center manually which depends legitimately on subjective experience. This paper presents a novel effective clustering method for finding density peaks (ECDP). We harness statistics-based methods with geometric features to attain the density peaks automatically and accurately. Our studies demonstrate that our approach can select the cluster center efficiently and effectively for massive datasets.
Intelligent Kernel K-Means is a fully unsupervised clustering algorithm based on kernel. It is able to cluster kernel matrix without any information regarding to the number of required clusters. Our experiment using g...
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Intelligent Kernel K-Means is a fully unsupervised clustering algorithm based on kernel. It is able to cluster kernel matrix without any information regarding to the number of required clusters. Our experiment using gene expression of human colorectal carcinoma had shown that the genes were grouped into three clusters. Global silhouette value and davies-bouldin index of the resulted clusters indicated that they are trustworthy and compact. To analyze the relationship between the clustered genes and phenotypes of clinical data, we performed correlation (CR) between each of three phenotypes (distant metastasis, cancer and normal tissues, and lymph node) with genes in each cluster of original dataset and permuted dataset. The result of the correlation had shown that Cluster 1 and Cluster 2 of original dataset had significantly higher CR than that of the permuted dataset. Among the three clusters, Cluster 3 contained smallest number of genes, but 16 out of 21 genes in that cluster were genes listed in Tumor Classifier List (TCL). (C) 2015 The Authors. Published by Elsevier B.V.
Intelligent Kernel K-Means is a fully unsupervised clustering algorithm based on kernel. It is able to cluster kernel matrix without any information regarding to the number of required clusters. Our experiment using g...
详细信息
Intelligent Kernel K-Means is a fully unsupervised clustering algorithm based on kernel. It is able to cluster kernel matrix without any information regarding to the number of required clusters. Our experiment using gene expression of human colorectal carcinoma had shown that the genes were grouped into three clusters. Global silhouette value and davies-bouldin index of the resulted clusters indicated that they are trustworthy and compact. To analyze the relationship between the clustered genes and phenotypes of clinical data, we performed correlation (CR) between each of three phenotypes (distant metastasis, cancer and normal tissues, and lymph node) with genes in each cluster of original dataset and permuted dataset. The result of the correlation had shown that Cluster 1 and Cluster 2 of original dataset had significantly higher CR than that of the permuted dataset. Among the three clusters, Cluster 3 contained smallest number of genes, but 16 out of 21 genes in that cluster were genes listed in Tumor Classifier List (TCL).
This work concerns a new method called fuzzy membership C-means (FMCMs) for segmentation of magnetic resonance images (MRI), and an efficient program implementation of it to the segmentation of MRI. Classical unsuperv...
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This work concerns a new method called fuzzy membership C-means (FMCMs) for segmentation of magnetic resonance images (MRI), and an efficient program implementation of it to the segmentation of MRI. Classical unsupervisedclustering methods including the FCM by Bezdek, suffer many problems that can be partially treated with a proper rule to construct the initial membership matrix to clusters. This work develops a specific method to construct the initial membership matrix to clusters in order to improve the strength of the clusters. The new FMCM is tested on a set of benchmarks and then the application to the segmentation of MR images is presented and compared with the results obtained using FCM. (c) 2008 Elsevier B.V. All rights reserved.
With the development of network and communication technology, large volumes of video data are generated every day. How to retrieve information efficiently from the video documents becomes a valuable issue. To address ...
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ISBN:
(纸本)0780386531
With the development of network and communication technology, large volumes of video data are generated every day. How to retrieve information efficiently from the video documents becomes a valuable issue. To address this problem, the contents of the digital videos should be annotated and indexed beforehand. Shot boundary detection is the first step for indexing and retrieving the contents from a video. Usually there exist both distinct and indistinct shot boundaries throughout a video, so the detection of these shots becomes a difficult task. According to this problem we propose an unsupervised clustering algorithm based on feature distance Io self-organize and dynamically analyse the video data, hereby segmenting the video hierarchically.
To overcome the vast computation of the standard support vector machines (SVMs), Lee and Mangasarian proposed reduced support vector machines (RSVM). But they select 'support vectors' randomly from the trainin...
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ISBN:
(纸本)0780376633
To overcome the vast computation of the standard support vector machines (SVMs), Lee and Mangasarian proposed reduced support vector machines (RSVM). But they select 'support vectors' randomly from the training set, and this will affect the test result. In this paper, we select some representative vectors as support vectors via a simple unsupervised clustering algorithm, and then apply the RSVM method on these vectors. The proposed method can get higher recognition accuracy with fewer support vectors compared to the original RSVM, with the advantage of reducing the running time significantly.
In this paper, a hybrid clustering and gradient descent approach is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available. The propos...
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In this paper, a hybrid clustering and gradient descent approach is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available. The proposed approach is composed of two steps: structure identification and parameter identification. In the process of structure identification, a clustering method is proposed to provide a systematic procedure to determine the number of fuzzy rules and construct an initial fuzzy model from the given input-output data. In the process of parameter identification, the gradient descent method is used to tune the parameters of the constructed fuzzy model to obtain a more precise fuzzy model from the given input-output data. Finally, two examples of nonlinear system are given to illustrate the effectiveness of the proposed approach.
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