Now showing items 1-9 of 9

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      A Novel Clustering Algorithm using K-means (CUK). The Islamic

      Alnaji, Khaled W.; Ashour, Wesam M. (Foundation of Computer Science, 2011)
      While K-means is one of the most well known methods to partition data set into clusters, it still has a problem when clusters are of different size and different density. K-means converges to one of many local minima. Many ...
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      A novel construction of connectivity graphs for clustering and visualization

      Barbakh, Wesam; Fyfe, Colin (World Scientific and Engineering Academy and Society (WSEAS), 2008)
      We [5, 6] have recently investigated several families of clustering algorithms. In this paper, we show how a novel similarity function can be integrated into one of our algorithms as a method of performing clustering and ...
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      Avoiding objects with few neighbors in the K-Means process and adding ROCK Links to its distance

      Alnabriss, Hadi A; Ashour, Wesam M. (International Journal of Computer Applications, 244 5 th Avenue,# 1526, New York, NY 10001, USA India, 2011)
      K-means is considered as one of the most common and powerful algorithms in data clustering, in this paper we're going to present new techniques to solve two problems in the K-means traditional clustering algorithm, the 1st ...
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      Clustering Cells Shape Descriptors Using K-Means vs. Genetic Algorithm

      Abushmmala, Faten; Alhanjouri, Mohammed A. (International Technology and Science Publications (UK), 2018)
      This paper interested in clustering red blood cells, these cells are in form of digital images of blood films, a comparison made between Genetic Algorithm (GA) and K-Means behavior/performance in clustering. The data set ...
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      Color based image segmentation using different versions of k-means in two spaces

      Abu Shmmala, Faten; Ashour, Wesam M. (2013)
      In this paper color based image segmentation is done in two spaces. First in LAB color space and second in RGB space all that done using three versions of K-Means: K-Means, Weighted K-Means and Inverse Weighted K-Means ...
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      Colour Based Segmentation of Red Blood Cells using K-means and Image Morphological Operations

      Abushmmala, Faten; Alhanjouri, Mohammed A. (INTERNATIONAL JOURNAL OF ADVANCED AND INNOVATIVE RESEARCH, 2013)
      This paper interested in clustering red blood cells, these cells are in form of digital images of blood films, a comparison made between Genetic Algorithm (GA) and K-Means behavior/performance in clustering. The data set ...
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      Improving Bregman k-means

      Ashour, Wesam M.; Fyfe, Colin (Inderscience Publishers Ltd, 2014)
      We review Bregman divergences and use them in clustering algorithms which we have previously developed to overcome one of the difficulties of the standard k-means algorithm which is its sensitivity to initial conditions ...
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      K-means algorithm with a novel distance measure

      Abudalfa, Shadi; Mikki, Mohammad (The Scientific and Technological Research Council of Turkey, 2013)
      In this paper, we describe an essential problem in data clustering and present some solutions for it. We investigated using distance measures other than Euclidean type for improving the performance of clustering. We also ...
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      Stemming effectiveness in clustering of arabic documents

      Ghanem, Osama A; Ashour, Wesam M. (Foundation of Computer Science, 2012)
      Clustering is an important task gives good results with information retrieval (IR), it aims to automatically put similar documents in one cluster. Stemming is an important technique, used as feature selection to reduce ...