Now showing items 1-17 of 17

    • Thumbnail

      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 ...
    • Thumbnail

      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 ...
    • Thumbnail

      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 ...
    • Thumbnail

      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 ...
    • Thumbnail

      Immediate reward reinforcement learning for clustering and topology preserving mappings

      Fyfe, Colin; Barbakh, Wesam (Springer, Berlin, Heidelberg, 2009)
      We extend a reinforcement learning algorithm which has previously been shown to cluster data. Our extension involves creating an underlying latent space with some pre-defined structure which enables us to create a topology ...
    • Thumbnail

      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 ...
    • Thumbnail

      Improving clustering using Bregman divergences

      Ashour, Wesam M.; Fyfe, Colin (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 ...
    • Thumbnail

      Inverse weighted clustering algorithm

      Barbakh, Wesam; Fyfe, Colin (UNIVERSITY OF PAISLEY, 2007)
      We discuss a new form of clustering which overcomes some of the problems of traditional K-means such as sensitivity to initial conditions. We illustrate convergence of the algorithm on a number of artificial data sets. We ...
    • Thumbnail

      Local vs global interactions in clustering algorithms: Advances over K-means

      Barbakh, Wesam; Fyfe, Colin (IOS Press, 2008)
      We discuss one of the shortcomings of the standard K-means algorithm–its tendency to converge to a local rather than a global optimum. This is often accommodated by means of different random restarts of the algorithm, ...
    • Non-standard parameter adaptation for exploratory data analysis

      Barbakh, Wesam; Wu, Ying; Fyfe, Colin (Springer, 2009)
      This book presents the fruits of several years of research into non-standard methods of adaptation for exploratory data analysis. This research resulted in the award of PhD to two of the authors, Dr Wesam Barbakh and Dr ...
    • Thumbnail

      Online clustering algorithms

      Barbakh, Wesam; Fyfe, Colin (World Scientific Publishing Company, 2008)
      We introduce a set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local ...
    • Performance functions and clustering algorithms

      Barbakh, Wesam; Fyfe, Colin (UNIVERSITY OF PAISLEY, 2006)
      We investigate the effect of different performance functions for measuring the performance of clustering algorithms and derive different algorithms depending on which performance algorithm is used. In particular, we show ...
    • Thumbnail

      Reservoir computing and data visualisation

      Ashour, Wesam M.; Wang, TD; Fyfe, Colin (2012)
      We consider the problem of visualisation of high dimensional multivariate time series. A data analyst in creating a two dimensional projection of such a time series might hope to gain some intuition into the structure of ...
    • Tailoring local and global interactions in clustering algorithms

      Barbakh, Wesam; Fyfe, Colin (University of Paisley, 2007)
    • Thumbnail

      Topological mappings of video and audio data

      Fyfe, Colin; Barbakh, Wesam; Ooi, Wei Chuan; Ko, Hanseok (World Scientific Publishing Company, 2008)
      We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM).1 But whereas the GTM is an ...
    • Thumbnail

      Topology-preserving mappings for data visualisation

      Pena, Marian; Barbakh, Wesam; Fyfe, Colin (Springer, Berlin, Heidelberg, 2008)
      We present a family of topology preserving mappings similar to the Self-Organizing Map (SOM) and the Generative Topographic Map (GTM). These techniques can be considered as a non-linear projection from input or data space ...
    • Thumbnail

      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. ...