Computing and Information Systems - Research Publications

Permanent URI for this collection

http://hdl.handle.net/11343/350

Filters

2006 - 2010 8
Reset filters

Settings
Statistics
Citations
Author: Kotagiri, Ramamohanarao × Author: Bezdek, James ×

Search Results

Now showing 1 - 8 of 8
  • Item
    Thumbnail Image
    Tensor space learning for analyzing activity patterns from video sequences
    Wang, L ; Leckie, C ; Wang, X ; Kotagiri, R ; Bezdek, J (IEEE, 2007-12-01)
  • Item
    Thumbnail Image
    EP-based robust weighting scheme for fuzzy SVMS
    Zhang, S ; Ramamohanarao, K ; Bezdek, JC (Australian Computer Society, 2010-12-01)
  • Item
    Thumbnail Image
    iVAT and aVAT: Enhanced Visual Analysis for Cluster Tendency Assessment
    Wang, L ; Nguyen, UTV ; Bezdek, JC ; Leckie, CA ; Ramamohanarao, K ; Zaki, MJ ; Yu, JX ; Ravindran, B ; Pudi, V (SPRINGER-VERLAG BERLIN, 2010)
  • Item
    Thumbnail Image
    Approximate spectral clustering
    WANG, L. ; LECKIE, C. ; KOTAGIRI, R. ; BEZDEK, J. (Springer Verlag, 2009)
  • Item
    Thumbnail Image
    Selective Sampling for Approximate Clustering of Very Large Data Sets
    WANG, L. ; BEZDEK, J. ; LECKIE, C. ; KOTAGIRI, R. ( 2008)
  • Item
    Thumbnail Image
    Approximate clustering in very large relational data
    Bezdek, JC ; Hathaway, RJ ; Huband, JM ; Leckie, C ; Kotagiri, R (WILEY, 2006-08)
  • Item
    Thumbnail Image
    Enhanced Visual Analysis for Cluster Tendency Assessment and Data Partitioning
    Wang, L ; Geng, X ; Bezdek, J ; Leckie, C ; Ramamohanarao, K (IEEE COMPUTER SOC, 2010-10)
  • Item
    Thumbnail Image
    Automatically Determining the Number of Clusters in Unlabeled Data Sets
    Wang, L ; Leckie, C ; Ramamohanarao, K ; Bezdek, J (Institute of Electrical and Electronics Engineers, 2009-03-01)
    One of the major problems in cluster analysis is the determination of the number of clusters in unlabeled data, which is a basic input for most clustering algorithms. In this paper, we investigate a new method called Dark Block Extraction (DBE) for automatically estimating the number of clusters in unlabeled data sets, which is based on an existing algorithm for Visual Assessment of Cluster Tendency (VAT) of a data set, using several common image and signal processing techniques. Its basic steps include 1) generating a VAT image of an input dissimilarity matrix, 2) performing image segmentation on the VAT image to obtain a binary image, followed by directional morphological filtering, 3) applying a distance transform to the filtered binary image and projecting the pixel values onto the main diagonal axis of the image to form a projection signal, and 4) smoothing the projection signal, computing its first-order derivative, and then detecting major peaks and valleys in the resulting signal to decide the number of clusters. Our DBE method is nearly “automatic,” depending on just one easy-to-set parameter. Several numerical and real-world examples are presented to illustrate the effectiveness of DBE.