Journal ArticleDOI
Validity index for crisp and fuzzy clusters
TLDR
A cluster validity index and its fuzzification is described, which can provide a measure of goodness of clustering on different partitions of a data set, and results demonstrating the superiority of the PBM-index in appropriately determining the number of clusters are provided.About:
This article is published in Pattern Recognition.The article was published on 2004-03-01. It has received 710 citations till now. The article focuses on the topics: Fuzzy clustering & Correlation clustering.read more
Citations
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Journal ArticleDOI
A novel combinatorial merge-split approach for automatic clustering using imperialist competitive algorithm
TL;DR: The comparison of the proposed algorithm's results with basic ICA, its three recently developed types and several state-of-art automatic clustering methods, shows AC-ICA's superiority in terms of the speed of convergence to the optimal solution and quality of the obtained solution.
Journal ArticleDOI
Approximate spectral clustering with utilized similarity information using geodesic based hybrid distance measures
TL;DR: Experiments on artificial datasets, well-known small/medium-size real datasets, and four large datasets, with different types of clusters show that the proposed geodesic based hybrid similarity criteria outperform traditional similarity criteria in terms of clustering accuracy and several cluster validity indices.
Journal ArticleDOI
A novel cluster validity index for fuzzy clustering based on bipartite modularity
TL;DR: A novel cluster validity index whose implementation is based on the membership degrees and improved bipartites modularity of bipartite network is proposed for the validation of partitions produced by the fuzzy c-means (FCM) algorithm, and the effectiveness and reliability of the proposal is superior to other indices.
Journal ArticleDOI
From clustering to clustering ensemble selection: A review
TL;DR: Clustering Ensemble as mentioned in this paper is a knowledge reuse approach to solve the challenges inherent in clustering, it seeks to explore results of high stability and robustness by composing computed solutions achieved by base clustering algorithms without access to the features.
Journal ArticleDOI
Evolutionary k-means for distributed data sets
TL;DR: This paper proposes the use of evolutionary algorithms to overcome the k-means limitations and, at the same time, to deal with distributed data.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Applied Multivariate Statistical Analysis
R. A. Johnson,Dean W. Wichern +1 more
TL;DR: In this article, the authors present an overview of the basic concepts of multivariate analysis, including matrix algebra and random vectors, as well as a strategy for analyzing multivariate models.
Journal ArticleDOI
Applied Multivariate Statistical Analysis.
TL;DR: In this article, the authors present an overview of the basic concepts of multivariate analysis, including matrix algebra and random vectors, as well as a strategy for analyzing multivariate models.