Some refinements of rough -means clustering

Peters, Georg

doi:10.1016/j.patcog.2006.02.002

Cited by 187 publications

(66 citation statements)

References 7 publications

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“…Peters [23] pointed out some potential pitfalls of the algorithm in terms of objective function and numerical stability and suggested some improvements to overcome these. Equation 10 is revised to…”

Section: Rough C-meansmentioning

confidence: 99%

Soft computing-based colour quantisation

Schaefer

2014

J Image Video Proc

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Soft computing techniques have shown much potential in a variety of computer vision and image analysis tasks. In this paper, an overview of recent soft computing approaches to the colour quantisation problem is presented. Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image. Those selected colours form a colour palette, while the resulting image quality is directly determined by the choice of colours in the palette. The use of generic optimisation techniques such as simulated annealing and soft computing-based clustering algorithms founded on fuzzy and rough set ideas to formulate colour quantisation algorithms is discussed. These methods are capable of deriving good colour palettes and are shown to outperform standard colour quantisation techniques in terms of image quality. Furthermore, a hybrid colour quantisation algorithm which combines a generic optimisation approach with a common clustering algorithm is shown to lead to improved image quality. Finally, it is demonstrated how optimisation-based colour quantisation can be employed in conjunction with a more appropriate measure for image quality.

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Section: Rough C-meansmentioning

confidence: 99%

Soft computing-based colour quantisation

Schaefer

2014

J Image Video Proc

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“…The rough K-means approach has been a subject of further research. Peters [14] discussed various refinements of Lingras and West's original proposal [5]. These included calculation of rough centroids and the use of ratios of distances as opposed to differences between distances similar to those used in the rough set based Kohonen algorithm described in [6].…”

Section: Rough Clusteringmentioning

confidence: 99%

“…These included calculation of rough centroids and the use of ratios of distances as opposed to differences between distances similar to those used in the rough set based Kohonen algorithm described in [6]. The rough K-means and its various extensions [12], [14] have been found to be effective in distance based clustering. However, there is no theoretical work that proves that rough K-means explicitly finds an optimal clustering scheme.…”

Section: Rough Clusteringmentioning

confidence: 99%

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Evolutionary and Iterative Crisp and Rough Clustering I: Theory

Joshi

Lingras

2009

Lecture Notes in Computer Science

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Abstract.Researchers have proposed several Genetic Algorithms (GA) based clustering algorithms for crisp and rough clustering. In this two part series of papers, we compare the effect of GA optimization on resulting cluster quality of K-means, GA K-means, rough K-means, GA rough K-means and GA rough K-medoid algorithms. In this first part, we present the theoretical foundation of the transformation of the crisp clustering K-means and K-medoid algorithms into rough and evolutionary clustering algorithms. The second part of the paper will present experiments with a real world data set, and a standard data set.

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“…A technique combining k-means and rough set approaches proposed in [13] introduced the concept of upper and lower bounds to the k-means centroid. An enhancement to this technique was proposed in [14]. But the main drawback is that these techniques do not address the problem of selection of initial parameters.…”

Section: Introductionmentioning

confidence: 99%

A rough set based rational clustering framework for determining correlated genes

Jeyaswamidoss¹,

Thangaraj²,

Ramar

et al. 2016

Acta Microbiologica et Immunologica Hungarica

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Cluster analysis plays a foremost role in identifying groups of genes that show similar behavior under a set of experimental conditions. Several clustering algorithms have been proposed for identifying gene behaviors and to understand their significance. The principal aim of this work is to develop an intelligent rough clustering technique, which will efficiently remove the irrelevant dimensions in a highdimensional space and obtain appropriate meaningful clusters. This paper proposes a novel biclustering technique that is based on rough set theory. The proposed algorithm uses correlation coefficient as a similarity measure to simultaneously cluster both the rows and columns of a gene expression data matrix and mean squared residue to generate the initial biclusters. Furthermore, the biclusters are refined to form the lower and upper boundaries by determining the membership of the genes in the clusters using mean squared residue. The algorithm is illustrated with yeast gene expression data and the experiment proves the effectiveness of the method. The main advantage is that it overcomes the problem of selection of initial clusters and also the restriction of one object belonging to only one cluster by allowing overlapping of biclusters.

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Some refinements of rough -means clustering

Cited by 187 publications

References 7 publications

Soft computing-based colour quantisation

Soft computing-based colour quantisation

Evolutionary and Iterative Crisp and Rough Clustering I: Theory

A rough set based rational clustering framework for determining correlated genes

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