Now showing items 1-9 of 9

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      An Initialization Method for the K-means Algorithm using RNN and Coupling Degree

      Ahmed, Alaa H; Ashour, Wesam M. (Foundation of Computer Science (FCS), 2011)
      Since K-means is widely used for general clustering, its performance is a critical point. This performance depends highly on initial cluster centers since it may converge to numerous local minima. In this paper a proposed ...
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      Clustering Algorithms in Echo State Networks

      Ashour, Wesam M.; Abu-Issa, Abdallatif S; Hellwich, Olaf (Science and Engineering Research Support Society, 2016)
      In this work, we develop a new method of setting the input to reservoir and reservoir to reservoir weights in echo state machines. We use a clustering technique which we have previously developed as a pre-processing stage ...
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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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      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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      Multi density DBSCAN

      Ashour, Wesam M.; Sunoallah, Saad (Springer, Berlin, Heidelberg, 2011)
      Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal ...
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      MULTI-DENSITY DBSCAN USING REPRESENTATIVES: MDBSCAN-UR.

      Ahmed, Rwand; Elzaza, Eman; Ashour, Wesam M. (2011)
      DBSCAN is one of the most popular algorithms for cluster analysis. It can discover clusters with arbitrary shape and separate noises. But this algorithm cannot choose its parameter according to distributing of dataset. It ...
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      New Density-Based Clustering Technique: GMDBSCAN-UR.

      Alhanjouri, Mohammed A.; Ahmed, Rwand D (2012)
      Density Based Spatial Clustering of Applications of Noise (DBSCAN) is one of the most popular algorithms for cluster analysis. It can discover clusters with arbitrary shape and separate noises. But this algorithm cannot ...
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      Visualising Data Using Reservoir with Clustering

      Ashour, Wesam M.; Fyfe, Colin (Science and Engineering Research Support Society, 2013)
      In this paper we illustrate a new method of visualizing and projecting time series data using reservoir computing with clustering algorithms. We show the advantages of using clustering with reservoir to visualize data. ...