Suchergebnisse - Determining the number of clusters in a data set

Effects of Resampling in Determining the Number of Clusters in a Data Set von Dangl, Rainer, Leisch, Friedrich

“… Using cluster validation indices is a widely applied method in order to detect the number of groups in a data set and as such a crucial step in the model validation process in clustering …”
Volltext

NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set von Charrad, Malika, Ghazzali, Nadia, Boiteau, Véronique, Niknafs, Azam

“… quality of clusters, the degree with which a clustering scheme fits a specific data set and the optimal number of clusters in a partitioning …”
Volltext

A Graph Theoretic Criterion for Determining the Number of Clusters in a Data Set von Krolak-Schwedt, Sabine, Eckes, Thomas

“… This article is concerned with procedures for determining the number of clusters in a data set …”
Volltext

Automatically Determining the Number of Clusters in Unlabeled Data Sets von Liang Wang, Leckie, C., Ramamohanarao, K., Bezdek, J.

“… In this paper we investigate a new method called DBE (dark block extraction) for automatically estimating the number of clusters in unlabeled data sets, which is based on an existing algorithm for visual assessment of cluster tendency (VAT …”
Volltext

An Examination of Indexes for Determining the Number of Clusters in Binary Data Sets von Dimitriadou, Evgenia, Dolničar, Sara, Weingessel, Andreas

“… The simulation includes 162 binary data sets that are clustered by two different algorithms and lead to recommendations on the number of clusters for each index under consideration. Index results are evaluated and their performance is compared and analyzed …”
Volltext

A new validation index for determining the number of clusters in a data set von Haojun Sun, Shengrui Wang, Qingshan Jiang

“… and detecting the best number of clusters for a given data set in practical applications. After a review of the relevant literature, we present the new validity function …”
Volltext

A New Similarity Measure and Its Use in Determining the Number of Clusters in a Multivariate Data Set von Vassiliou, A., Tambouratzis, D. G., Koutras, M. V., Bersimis, S.

“… Krolak-Schwerdt and Eckes [Krolak-Schwerdt, S., Eckes, T. (1992). A graph theoretic criterion for determining the number of cluster in a data set. Multivariate Behav. Res. 27(4):541-565 …”
Volltext

An automatic method to determine the number of clusters using decision-theoretic rough set von Yu, Hong, Liu, Zhanguo, Wang, Guoyin

“… renewed interest in clustering as a tool for the analysis of large data sets in many fields. Determining the number of clusters in a data set is one of the most challenging and difficult problems in cluster analysis …”
Volltext

A Multicriteria Decision Making Approach for Estimating the Number of Clusters in a Data Set von Peng, Yi, Zhang, Yong, Kou, Gang, Shi, Yong

“… Determining the number of clusters in a data set is an essential yet difficult step in cluster analysis …”
Volltext

FCM-Based Model Selection Algorithms for Determining the Number of Clusters von Sun, Haojun, Wang, Shengrui, Jiang, Qingshan

“… After a brief review of the relevant literature, we present a new algorithm for determining the number of clusters in a given data set and a new validity index for measuring the “goodness” of clustering. Experimental results and comparisons are given to illustrate the performance of the new algorithm …”
Volltext

MCS: A Method for Finding the Number of Clusters von Albatineh, Ahmed N., Niewiadomska-Bugaj, Magdalena

“… This paper proposes a maximum clustering similarity (MCS) method for determining the number of clusters in a data set by studying the behavior of similarity indices comparing two (of several) clustering methods …”
Volltext

A new procedure to optimize the selection of groups in a classification tree: Applications for ecological data von Guidi, Lionel, Ibanez, Frédéric, Calcagno, Vincent, Beaugrand, Grégory

“… The use of multiple measurements in taxonomic problems. Ann. Eugenic. 7, 179–188], (2) simulated data with predetermined numbers of clusters following Milligan and Cooper …”
Volltext

Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters von Li, M.J., Ng, M.K., Cheung, Y.-m., Huang, J.Z.

“… Combined with cluster validation techniques, the new algorithm can determine the number of clusters in a data set, which is a well known problem in k-means clustering …”
Volltext

Estimating the number of clusters in a numerical data set via quantization error modeling von Kolesnikov, Alexander, Trichina, Elena, Kauranne, Tuomo

“… We propose a new method for determining an optimal number of clusters in the data set. The method is based on parametric modeling of the quantization error …”
Volltext

Fuzziness parameter selection in fuzzy c-means： The perspective of cluster validation von Zhou, KaiLe, Fu, Chao, Yang, ShanLin

“… Cluster validity index （CVI） is a kind of criterion function to validate the clustering results, thereby determining the optimal cluster number of a data set …”
Volltext

A Novel Approach for Gaussian Mixture Model Clustering Based on Soft Computing Method von Gogebakan, Maruf

“… Determining the number of clusters in a data set is a significant and difficult problem in cluster analysis …”
Volltext

The impact of estimator choice: Disagreement in clustering solutions across K estimators for Bayesian analysis of population genetic structure across a wide range of empirical data sets von Stankiewicz, Kathryn H., Vasquez Kuntz, Kate L., Baums, Iliana B.

“… If this finding holds for empirical data sets, conclusions about the scale of gene flow may have to be revised for a large number of studies …”
Volltext

Determining the number of clusters using information entropy for mixed data von Liang, Jiye, Zhao, Xingwang, Li, Deyu, Cao, Fuyuan, Dang, Chuangyin

“… In cluster analysis, one of the most challenging and difficult problems is the determination of the number of clusters in a data set, which is a basic input parameter for most clustering algorithms …”
Volltext

Fuzzy and hard clustering analysis for thyroid disease von Azar, Ahmad Taher, El-Said, Shaimaa Ahmed, Hassanien, Aboul Ella

“… This paper proposes a comparison between hard and fuzzy clustering algorithms for thyroid diseases data set in order to find the optimal number of clusters …”
Volltext

Robust cluster analysis of microarray gene expression data with the number of clusters determined biologically von Bickel, David R.

“… The measure can be made robust using a rank order correlation coefficient. A robust graphical method of summarizing the results of cluster analysis and a biological method of determining the number of clusters are also presented …”
Volltext