VQ-agglomeration: a novel approach to clustering

Wang, J.-H.; Rau, Jen-Da

doi:10.1049/ip-vis:20010139

Cited by 19 publications

(7 citation statements)

References 17 publications

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“…We report the results for the three most commonly used measures: single, complete and average linkage. Finally, we run mclust for several number of clusters as well (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) and the Mclust function itself chooses the one that maximizes the bic criteria. We wish to assess the performance of the different methods regarding (i) their ability to estimate the number of clusters and (ii) their ability to find the clusters themselves (clusters strength).…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The idea is to iteratively move the data points toward the cluster centers and to use the number of different Correspondence to: Júlia Viladomat (juliavc@stanford.edu) limiting points as an estimate for the number of clusters. In this sense, gravitational clustering [8][9][10][11] assumes that the data points are particles of unit mass with zero velocity that move toward cluster centers as a result of gravitational forces. Furthermore, mean-shift clustering [12][13][14][15][16][17] uses kernel functions in density estimation to move data points toward denser areas.…”

Section: Introductionmentioning

confidence: 99%

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Nearest‐neighbors medians clustering

Peña

Viladomat

Zamar

2012

Statistical Analysis

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We propose a nonparametric cluster algorithm based on local medians. Each observation is substituted by its local median and this new observation moves toward the peaks and away from the valleys of the distribution. The process is repeated until each observation converges to a fixpoint. We obtain a partition of the sample based on the convergence points. Our algorithm determines the number of clusters and the partition of the observations given the proportion α of neighbors. A fast version of the algorithm where only a subset of the observations from the sample is processed is also proposed. A proof of the convergence from each point to its closest fixpoint and the existence and uniqueness of a fixpoint in a neighborhood of each mode is given for the univariate case. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nearest‐neighbors medians clustering

Peña

Viladomat

Zamar

2012

Statistical Analysis

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“…Partitional clustering in general has two types, one is crisp clustering and the other is fuzzy clustering. While each data point fits into only one cluster in crisp clustering, fuzzy clustering may put each data point into more than one cluster to some extent (Frigui & Krishnapuram, 1999;Wang & Rau, 2007). In case of overlapping, fuzzy clustering can deal with boundaries to solve this problem (Zhang & Shen, 2014).…”

Section: Introductionmentioning

confidence: 99%

A novel data clustering algorithm based on gravity center methodology

Kuwil

Atila

Abu-Issa

et al. 2020

Expert Systems with Applications

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The concept of clustering is to separate clusters based on the similarity which is greater within cluster than among clusters. The similarity consists of two principles, namely, connectivity and cohesion. However, in partitional clustering, while some algorithms such as K-means and K-medians divides the dataset points according to the first principle (connectivity) based on centroid clusters without any regard to the second principle (cohesion), some others like K-medoids partially consider cohesion in addition to connectivity. This prevents to discover clusters with convex shape and results are affected negatively by outliers. In this paper a new Gravity Center Clustering (GCC) algorithm is proposed which depends on critical distance (λ) to define threshold among clusters. The algorithm falls under partition clustering and is based on gravity center which is a point within cluster that verifies both the connectivity and cohesion in determining the similarity of each point in the dataset. Therefore, the proposed algorithm deals with any shape of data better than K-means, K-medians and K-medoids. Furthermore, GCC algorithm does not need any parameters beforehand to perform clustering but can help user improving the control over clustering results and deal with overlapping and outliers providing two coefficients and an indicator. In this study, 22 experiments are conducted using different types of synthetic, and real healthcare datasets. The results show that the proposed algorithm satisfies the concept of clustering and provides great flexibility to get the optimal solution especially since clustering is considered as an optimization problem.

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“…There are mainly two approaches to move data points toward cluster centers. The first approach, gravitational clustering (Wright 1977;Kundu 1999;Sato 2000;Wang and Rau 2001), can be interpreted from the point of view of field theory in physics. In these algorithms, each data point is considered as a particle of unit mass with zero velocity and it is gradually moving toward the cluster center due to the gravitation.…”

Section: Introductionmentioning

confidence: 99%

CLUES: A non-parametric clustering method based on local shrinking

Wang

Qiu

Zamar

2007

Computational Statistics & Data Analysis

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In this paper, we propose a new non-parametric clustering method based on local shrinking. Each data point is transformed in such a way that it moves a specific distance toward a cluster center. The direction and the associated size of each movement are determined by the median of its K-nearest neighbors. This process is repeated until a pre-defined convergence criterion is satisfied. The optimal value of the K is decided by optimizing index functions that measure the strengths of clusters. The number of clusters and the final partition are determined automatically without any input parameter except the stopping rule for convergence. Our performance studies have shown that this algorithm converges fast and achieves high accuracy.

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VQ-agglomeration: a novel approach to clustering

Cited by 19 publications

References 17 publications

Nearest‐neighbors medians clustering

Nearest‐neighbors medians clustering

A novel data clustering algorithm based on gravity center methodology

CLUES: A non-parametric clustering method based on local shrinking

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