The MinMax k-Means clustering algorithm

Tzortzis, Grigorios; Likas, Aristidis

doi:10.1016/j.patcog.2014.01.015

Cited by 228 publications

(103 citation statements)

References 21 publications

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“…Tzortzis et al [5] have introduced a method that integrates MinMax and K-means algorithms, to assign weights to the clusters with respect to their variance and optimized a weighted version of the k-Means. Weights are trained iteratively along with the cluster assignments.…”

Section: B Centroid Based Clusteringmentioning

confidence: 99%

An Enhanced Equilin Multi-Clustering Algorithm for Constructing Numeric Clusters

A¹,

S²

2016

Int. Jour. Info. Sci. Comp.

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Clustering techniques are often applied in data analytics for interpreting the similarities within data objects over large datasets. Despite the existence of many clustering algorithm in the literature such as connectivity, centroid, distribution, density etc, the factor that constitutes a cluster are different from one another. However, the success of clustering depends upon the maximization of intra-cluster similarity and inter-cluster dissimilarity. The significant implication of clustering algorithms in many real-world applications emerges the proposal of newer algorithms. As a consequence, in this paper, a novel effort is made to generate clusters from a different aspect of grouping data objects using multidimensional geographical linear equation called Equilin Clustering. The technique incorporates the standard linear equation and the method of percentage split for clustering numerical data. The results show that the performance of Equilin Clustering yields better cluster results with reduced complexity over time and number of iterations.

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Section: B Centroid Based Clusteringmentioning

confidence: 99%

An Enhanced Equilin Multi-Clustering Algorithm for Constructing Numeric Clusters

A¹,

S²

2016

Int. Jour. Info. Sci. Comp.

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“…Although for different data, it can learn its own Mahalanobis distance, this comes at high computational cost. Meanwhile, covariance describes the linear relationship between two variables, it is not universally for all kinds of data [2,5].…”

Section: Introductionmentioning

confidence: 99%

“…Another important shortage that restricts the application of K-means is the initial cluster centroids and the number of clusters K must be decided before analysis [1][2][3][4][5][6][7][8]. The analysis results heavily depend on the initial positions of the cluster centers, thus if a bad initialization is chosen the objective function is easily getting trapped in poor local minima [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…In the meantime, cluster analysis is unsupervised learning which means there are no any category labels that tag objects with prior identifiers, thus it is very hard to find a proper K. For this issue,several heuristic methods have been given. However, the most widely used method is running the algorithm independently for different values of K and to find the most meaningful partition [5,6,7,8]. It is a kind of dynamic strategy.…”

Section: Introductionmentioning

confidence: 99%

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An Improved K-means Method with Density Distribution Analysis

Xue

Wang

2018

MATEC Web Conf.

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Abstract. In this paper, a novel K-means clustering algorithm is proposed. Before running the traditional Kmeans, the cluster centers should be randomly selected, which would influence the time cost and accuracy. To solve this problem, we utilize density distribution analysis in the traditional K-means. For a reasonable cluster, it should have a dense inside structure which means the points in the same cluster should tightly surround the center, while separated away from other cluster canters. Based on this assumption, two quantities are firstly introduced: the local density of cluster center ρi and its desperation degree δi, then some reasonable cluster centers candidates are selected from the original data. We performed our algorithm on three synthetic data and a real bank business data to evaluate its accuracy and efficiency. Comparing with Traditional Kmeans and K-means++, the results demonstrated that the improved method performs better.

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“…it uses the distances between each data points as the weights to build Huffman tree, and then chooses the cluster center according to the value of k in the reverse order of Huffman tree .In literature [3][4] [5],in order to make the selected initial cluster centers far away from each other, the maximum distance method and the maximum and minimum distance method are utilized respectively. In literature [11], using this method effectively overcomes the issue of the algorithm being sensitive to the initial cluster centers, the method ensures the efficiency of the algorithm even in the situations where the center of initial cluster chosen randomly is bad. In literature [6] [7][12],combined with the method of minimum spanning tree, based on the distance between the data points, a minimum spanning tree is constructe.…”

Section: Introductionmentioning

confidence: 99%

K-means Algorithm Based on Fitting Function

Chu¹,

Deng²,

Linli³

2015

Proceedings of the 2015 International Conference on Applied Science and Engineering Innovation

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Abstract. The K-means algorithm has the shortcomings of being sensitive to the initial clustering center, and in order to overcome this drawback, in this paper ,on the basis of the combination of data density and the optimal distance , a new definition of fitting function is made and then a kind of K-means algorithm based on fitting function is proposed. By utilizing the fitting function to select the initial clustering center, the selection of the initial cluster centers can be made as much close to the real sample clustering centers as possible. The experiments proved that, the K-means algorithm based on fitting function reduces the number of iterations and enhances the stability of the algorithm, as well as improves the efficiency of the algorithm.

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The MinMax k-Means clustering algorithm

Cited by 228 publications

References 21 publications

An Enhanced Equilin Multi-Clustering Algorithm for Constructing Numeric Clusters

An Enhanced Equilin Multi-Clustering Algorithm for Constructing Numeric Clusters

An Improved K-means Method with Density Distribution Analysis

K-means Algorithm Based on Fitting Function

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