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This paper is an application of medical image processing. Multimodality medical images are widely used nowadays for disease diagnosis. The CT/PET medical images are used in this paper for the segmentation of liver anomalies. The preprocessing was done by median filter and the segmentation was performed by binary tree quantization algorithm. The binary tree quantization algorithm produces better results...
This paper proposes a method to cluster documents of variable length. The main idea is to apply (a) automatic identification of 1, 2, and 3 grams (To reduce the dependency on huge background vocabulary support or learning or complex probabilistic approach), (b) order them by some measure of relevance, which is developed with the help of Tf-Idf and Term-Weighting approach, and finally (c) use them...
In the literature on subspace clustering, traditional clustering techniques have been extended for computing meaningful and interesting clusters in the appropriate subspaces of the high dimensional data. We present a novel algorithm to capture unobserved object relationships embedded in fuzzy subspaces. In order to model the uncertainties of fuzzy data, we propose a modification of fuzzy c-means algorithm...
Semisupervised clustering algorithms partition a given data set using limited supervision from the user. The success of these algorithms depends on the type of supervision and also on the kind of dissimilarity measure used while creating partitions of the space. This paper proposes a clustering algorithm that uses supervision in terms of relative comparisons, viz., x is closer to y than to z. The...
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