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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 5    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 5,May 2012
An Optimal iterative Minimal Spanning tree Clustering Algorithm for images[
Full Text(PDF, )  PP.I003-I008  
Author(s)
S. Senthil and Dr.R.David Chandrakumar
KEYWORDS
: Euclidean minimum spanning tree, clustering, eccentricity, center hierarchical clustering, sub tree, standard deviation, cluster separation
ABSTRACT
Limited Spatial resolution, poor contrast, overlapping intensities, noise and intensity in homogeneities variation make the assignment of segmentation of medical images is greatly difficult. In recent days, mathematical algorithm supported automatic segmentation system plays an important role in clustering of imaging. The minimal spanning tree algorithm is capable of detecting clustering with irregular boundaries. In this paper we propose an optimal iterative minimal spanning tree clustering algorithm (OPIMSTCA).At each hierarchical level, it optimizes the number of cluster, from which the proper hierarchical structure of underlying data set can be found. The algorithm uses a new cluster validation criterion based on the geometric property of data partition of the data set in order to find the proper number of clusters at each level. The center and standard deviation of the cluster are computed to find the tightness of the individual clusters. In this paper we compute tightness of clusters, which reflects good measure of the efficacy of clustering. The algorithm works in two phases. The first phase of the algorithm produces sub trees. The second phase creates objective function using optimal number of clusters. The performance of proposed method has been shown with random data and then the new Cluster separation approach to optimal number of clustering. The experimental results demonstrate that our proposed method is a promising technique for effective optimal clusters.
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