Search - validity cluster

Search - validity cluster - List

[AI-NN-PR] FuzzyClusteringToolbox DL : 0: 这是一个关于FCM,GG,GK算法的源码，里面包含PC,PE,XB聚类有效性度量的matlab源码，并含有程序说明文档-This is the one on FCM, GG, GK algorithm source code, which contains PC, PE, XB cluster validity measure of the matlab source code, and contains procedures for documentation
Update : 2025-04-26 Size : 2mb Publisher : yyifang
[Industry research] Studies-on-Fuzzy-C-Means-Based-on-Ant-Colony-Algo DL : 0: A fault identification with fuzzy C-Mean clustering algorithm based on improved ant colony algorithm (ACA) is presented to avoid local optimization in iterative process of fuzzy C-Mean (FCM) clustering algorithm and the difficulty in fault classification. In the algorithm, the problem of fault identification is translated to a constrained optimized clustering problem. Using heuristic search of colony can find good solutions. And according to the information content of cluster center, it could merger surrounding data together to cause clustering identification. The algorithm may identify fuzzy clustering numbers and initial clustering center. It can also prevent data classification from causing some errors. Thus, applying in fault diagnosis shows validity of computing and credibility of identification results.
Update : 2025-04-26 Size : 267kb Publisher : rishi
[JSP/Java] clusters DL : 0: This application demonstrates the usage of the cluster validity measures
Update : 2025-04-26 Size : 11kb Publisher : luca
[File Format] 9552010E202 DL : 0: This paper presents a new cluster validity index for nding a suitable number of fuzzy clusters with crisp and fuzzy data. The new index, called the ECAS-index, contains exponential compactness and separation measures. These measures indicate homogeneity within clusters and heterogeneity between clusters, respectively. Moreover, a fuzzy c-mean algorithm is used for fuzzy clustering with crisp data, and a fuzzy k-numbers clustering is used for clustering with fuzzy data. In comparison to other indices, it is evident that the proposed index is more effective and robust under different conditions of data sets, such as noisy environments and large data sets.
Update : 2025-04-26 Size : 3.31mb Publisher : m
[Other] cluster-validity-measure-and-its-applicatio DL : 0: cluster validity measure and its applicatio
Update : 2025-04-26 Size : 1.01mb Publisher : Soufi
[Other] Cluster-Validity DL : 0: Comparison and Evaluation of Different Cluster Validity
Update : 2025-04-26 Size : 38kb Publisher : Soufi
[Other] Validity-Measures-for-the-Fuzzy-Cluster DL : 0: Validity Measures for the Fuzzy Cluster
Update : 2025-04-26 Size : 490kb Publisher : Soufi
[Other] Some-New-Indexes-of-Cluster-Validity DL : 0: Some New Indexes of Cluster Validity
Update : 2025-04-26 Size : 734kb Publisher : Soufi
[Other] On-Cluster-Validity-for-the-Fuzzy-c-Means DL : 0: On Cluster Validity for the Fuzzy c-Means
Update : 2025-04-26 Size : 883kb Publisher : Soufi
[Algorithm] NCestimation_V2 DL : 0: The NCT includes 4 External validity indices and 8 internal validity indices, and the sub-routine validity_Index.m is designed to use them. This tool is suitable for the research work such as the performance comparison of different indices on estimation of the number of clusters, algorithm design by improving part codes of this toolbox. i) External validity indices when true class labels are known: Rand index Adjusted Rand index Mirkin index Hubert index ii) Internal validity indices when true class labels are unknown: Silhouette Davies-Bouldin Calinski-Harabasz Krzanowski-Lai Hartigan weighted inter- to intra-cluster ratio Homogeneity Separation iii) Others Error rate (compared with true labels) System Evolution: it is used to estimate the number of clusters and give separable degrees between clusters.-The NCT includes 4 External validity indices and 8 internal validity indices, and the sub-routine validity_Index.m is designed to use them. This tool is suitable for the research work such as the performance comparison of different indices on estimation of the number of clusters, algorithm design by improving part codes of this toolbox. i) External validity indices when true class labels are known: Rand index Adjusted Rand index Mirkin index Hubert index ii) Internal validity indices when true class labels are unknown: Silhouette Davies-Bouldin Calinski-Harabasz Krzanowski-Lai Hartigan weighted inter- to intra-cluster ratio Homogeneity Separation iii) Others Error rate (compared with true labels) System Evolution: it is used to estimate the number of clusters and give separable degrees between clusters.
Update : 2025-04-26 Size : 36kb Publisher : taqwa