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Algorithms for clustering data

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The article was published on 1988-01-01 and is currently open access. It has received 8586 citations till now. The article focuses on the topics: Cluster analysis & Correlation clustering.

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A Survey of Multiobjective Evolutionary Clustering

TL;DR: A comprehensive and critical survey of the multitude of multiobjective evolutionary clustering techniques existing in the literature, classified according to the encoding strategies adopted, objective functions, evolutionary operators, strategy for maintaining nondominated solutions, and the method of selection of the final solution.
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On clustering and retrieval of video shots through temporal slices analysis

TL;DR: Based on the analysis of temporal slices, novel approaches for clustering and retrieval of video shots are proposed, found to be useful particularly for sports games, where motion and color are important visual cues when searching and browsing the desired video shots.
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Clusters in a group : Decision making in the vector space formulation of the analytic hierarchy process

TL;DR: An algorithm to group individuals into natural clusters using a convenient similarity measure is discussed and implemented and the cohesiveness of a homogeneous group or cluster is investigated.
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Multiclass classification of microarray data with repeated measurements: application to cancer

TL;DR: It is shown that removing highly correlated genes typically improves classification results using a small set of genes, and the uncorrelated shrunken centroid (USC) andEWUSC algorithms are developed.
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Vaginal Microbiota of Adolescent Girls Prior to the Onset of Menarche Resemble Those of Reproductive-Age Women

TL;DR: The findings suggest that the vaginal microbiota of girls begin to resemble those of adults well before the onset of menarche, and may better inform clinical approaches to vulvovaginal care of adolescent girls.
References
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Book

Clustering Algorithms

Journal ArticleDOI

Shortest connection networks and some generalizations

TL;DR: In this paper, the basic problem of interconnecting a given set of terminals with a shortest possible network of direct links is considered, and a set of simple and practical procedures are given for solving this problem both graphically and computationally.
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An examination of procedures for determining the number of clusters in a data set

TL;DR: A Monte Carlo evaluation of 30 procedures for determining the number of clusters was conducted on artificial data sets which contained either 2, 3, 4, or 5 distinct nonoverlapping clusters to provide a variety of clustering solutions.
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SLINK: An optimally efficient algorithm for the single-link cluster method

TL;DR: Sibson gives an O(n 2) algorithm for single-linkage clustering, and proves that this algorithm achieves the theoretically optimal lower time bound for obtaining a single- linkage dendrogram.