Now showing items 1-20 of 59

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      A dynamic method for discovering density varied clusters

      Elbatta, Mohammed TH; Ashour, Wesam M. (Hindawi Limited, 2013)
      Density-based spatial clustering of applications with noise (DBSCAN) is a base algorithm for density based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is ...
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      A family of novel clustering algorithms

      Barbakh, Wesam; Crowe, Malcolm; Fyfe, Colin (Springer, Berlin, Heidelberg, 2006)
      We review the performance function associated with the familiar K-Means algorithm and that of the recently developed K-Harmonic Means. The inadequacies in these algorithms leads us to investigate a family of performance ...
    • A Modified DBSCAN Clustering Algorithm.

      Elkourd, Amer M; Ashour, Wesam M. (2011)
      DBSCAN is one of the most famous clustering algorithms that is based on density clustering. it can find clusters of arbitrary shapes. A major limitation of DBSCAN is its sensitivity to the input parameters also it cannot ...
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      A New Model in Arabic Text Classification Using BPSO/REP-Tree

      Naji, Hamza A.; Alhanjouri, Mohammed A.; Ashour, Wesam M. (الجامعة الإسلامية - غزة, 2017)
      Specifying an address or placing a specific classification to a page of text is an easy process somewhat, but what if there were many of these pages needed to reach a huge amount of documents. The process becomes difficult ...
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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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      A vibration method for discovering density varied clusters

      Elbatta, Mohammad; Bolbol, Raed M; Ashour, Wesam M. (Hindawi Publishing Corporation, 2011)
      DBSCAN is a base algorithm for density-based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers. However, it is fail to handle the ...
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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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      Arabic morphological tools for text mining

      Saad, Motaz K; Ashour, Wesam M. (2010)
      Arabic Language has complex morphology; this led to unavailability to standard Arabic morphological analysis tools until now. In this paper, we present and evaluate existing common Arabic stemming/light stemming algorithms, ...
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      Arabic text classification using decision trees

      Saad, Motaz K; Ashour, Wesam M. (2010)
      Text mining draw more and more attention recently, it has been applied on different domains including web mining, opinion mining, and sentiment analysis. Text pre-processing is an important stage in text mining. The major ...
    • AVOIDING NOISE AND OUTLIERS IN K-MEANS.

      Jnena, Rami; Timraz, Mohammed; Ashour, Wesam M. (2011)
      Applying k-means algorithm on the datasets that include large number of noise and outlier objects, gives unclear clusters results. In this paper we proposed a new technique for avoiding these noise and outliers by applying ...
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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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      BH-centroids: A New Efficient Clustering Algorithm

      Elfarra, Belal K; EL Khateeb, Tayseer J; Ashour, Wesam M. (Science and Engineering Research Support Society, 2013)
      The k-means algorithm is one of most widely used method for discovering clusters in data; however one of the main disadvantages to k-means is the fact that you must specify the number of clusters as an input to the algorithm. ...
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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 large-scale data based on modified affinity propagation algorithm

      Serdah, Ahmed M; Ashour, Wesam M. (De Gruyter Open, 2016)
      Traditional clustering algorithms are no longer suitable for use in data mining applications that make use of large-scale data. There have been many large-scale data clustering algorithms proposed in recent years, but most ...
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      Clustering Using Optimized Gaussian Kernel Function

      El-Bhissy, Kanaan; El-Faleet, Fadi; Ashour, Wesam M. (SERSC, 2014)
      Clustering and segmentation algorithms that depend on Gaussian kernel function as a way for constructing affinity matrix, these algorithms like spectral clustering algorithms suffer from the poor estimation of parzen window ...
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      Clustering with alternative similarity functions

      Barbakh, Wesam; Fyfe, Colin (World Scientific and Engineering Academy and Society (WSEAS), 2008)
      We [6, 7] 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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      Clustering with reinforcement learning

      Barbakh, Wesam; Fyfe, Colin (Springer, Berlin, Heidelberg, 2007)
      We show how a previously derived method of using reinforcement learning for supervised clustering of a data set can lead to a sub-optimal solution if the cluster prototypes are initialised to poor positions. We then develop ...
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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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      Combining IWC and PSO to Enhance Data Clustering

      Skaik, Ahmed Z.; Ashour, Wesam M. (الجامعة الإسلامية - غزة, 2017)
      In this paper we propose a clustering method based on combination of the Particle Swarm Optimization (PSO) and the inverse weighted clustering algorithm IWC, It is shown how PSO can be used to find the centroids of a user ...