New methods for the initialisation of clusters

Al-Daoud, Moh B.; Roberts, Stuart

doi:10.1016/0167-8655(95)00119-0

Cited by 64 publications

(27 citation statements)

References 9 publications

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“…At the same time, the selected values have to be significantly smaller than the number of objects in the data sets, which is the main motivation for performing data clustering. Reported studies [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] on K-means clustering and its applications usually do not contain any explanation or justification for selecting particular values for K. Table 1 lists the numbers of clusters and objects and the corresponding data sets used in those studies. Two observations could be made when analysing the data in the table.…”

Section: Values Of K Specified Within a Range Or Setmentioning

confidence: 99%

Selection of K in K-means clustering

Pham

Dimov

Nguyen

2005

Proceedings of the Institution of Mechanical Engineers, Part C:

451

247

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The K-means algorithm is a popular data-clustering algorithm. However, one of its drawbacks is the requirement for the number of clusters, K, to be specified before the algorithm is applied. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Factors that affect this selection are then discussed and a new measure to assist the selection is proposed. The paper concludes with an analysis of the results of using the proposed measure to determine the number of clusters for the K-means algorithm for different data sets.

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Section: Values Of K Specified Within a Range Or Setmentioning

confidence: 99%

Selection of K in K-means clustering

Pham

Dimov

Nguyen

2005

Proceedings of the Institution of Mechanical Engineers, Part C:

451

247

View full text Add to dashboard Cite

show abstract

“…Methods for automatic initialization of clusters have been proposed in the literature [10 -12]. Al-Daoud and Roberts [10] proposed two methods, the first of which picks points randomly in evenly spaced cells across the entire histogram of the data and reduces the number until the required seeds are found. The second method tries to optimize the sum of squares of the distances from the cluster centres.…”

Section: Introductionmentioning

confidence: 99%

Automatic seed initialization for the expectation-maximization algorithm and its application in 3D medical imaging

Lynch

Ilea

Robinson

et al. 2007

Journal of Medical Engineering & Technology

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Statistical partitioning of images into meaningful areas is the goal of all region-based segmentation algorithms. The clustering or creation of these meaningful partitions can be achieved in number of ways but in most cases it is achieved through the minimization or maximization of some function of the image intensity properties. Commonly these optimization schemes are locally convergent, therefore initialization of the parameters of the function plays a very important role in the final solution. In this paper we perform an automatically initialized expectation-maximization algorithm to partition the data in medical MRI images. We present analysis and illustrate results against manual initialization and apply the algorithm to some common medical image processing tasks.

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“…The assignment should include a vector quantization request; Katsavounidis variant obtains the point with the most Euclidean standard as the first center. AlDaoud's density-based method first regularly partitions the data space into M decomposed hyper-cubes [27]. Then this randomly selects K Nm/N points of hypercube m (m {1,2,…,M}) to create a number of K centers where Nm is the number of points in hypercube m. Bradley and Fayyad's method [28] begins by randomly partitioning the data set into J subsets.…”

Section: IImentioning

confidence: 99%

Face Extraction from Image based on K-Means Clustering Algorithms

Farhang¹

2017

ijacsa

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Abstract-This paper proposed a new application of K-means clustering algorithm. Due to ease of implementation and application, K-means algorithm can be widely used. However, one of the disadvantages of clustering algorithms is that there is no balance between the clustering algorithm and its applications, and many researchers have paid less attention to clustering algorithm applications. The purpose of this paper is to apply the clustering algorithm application to face extraction. An improved K-means clustering algorithm was proposed in this study. A new method was also proposed for the use of clustering algorithms in image processing. To evaluate the proposed method, two case studies were used, including four standard images and five images selected from LFW standard database. These images were reviewed first by the K-means clustering algorithm and then by the RER-K-means and FE-RER-clustering algorithms. This study showed that the K-means clustering algorithm could extract faces from the image and the proposed algorithm used for this work increased the accuracy rate and, at the same time, reduced the number of iterations, intra cluster distance, and the related processing time.

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New methods for the initialisation of clusters

Cited by 64 publications

References 9 publications

Selection of K in K-means clustering

Selection of K in K-means clustering

Automatic seed initialization for the expectation-maximization algorithm and its application in 3D medical imaging

Face Extraction from Image based on K-Means Clustering Algorithms

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