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- W2201719130 abstract "The present research proposes a paradigm for the clustering of data in which no prior knowledge about the number of clusters is required. Here shape based similarity is used as an index of similarity for clustering. The paper exploits the pattern identification prowess of Hidden Markov Model (HMM) and overcomes few of the problems associated with distance based clustering approaches. In the present research partitioning of data into clusters is done in two steps. In the first step HMM is used for finding the number of clusters then in the second step data is classified into the clusters according to their shape similarity. Experimental results on synthetic datasets and on the Iris dataset show that the proposed algorithm outperforms few commonly used clustering algorithm." @default.
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- W2201719130 date "2013-01-01" @default.
- W2201719130 modified "2023-10-16" @default.
- W2201719130 title "A Novel Clustering Approach Using Shape Based Similarity" @default.
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- W2201719130 doi "https://doi.org/10.1007/978-3-642-32063-7_3" @default.
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