Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-32063-7_3 | INTELLIGENT INFORMATICS |
Keywords | Field | DocType |
Clustering,Hidden Markov Model,Shape Based similarity | Fuzzy clustering,Cluster (physics),Spectral clustering,Analytical chemistry,Pattern recognition,Correlation clustering,Computer science,Artificial intelligence,Iris flower data set,Cluster analysis,Hidden Markov model,Single-linkage clustering | Conference |
Volume | ISSN | Citations |
182 | 2194-5357 | 1 |
PageRank | References | Authors |
0.35 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Smriti Srivastava | 1 | 137 | 19.60 |
| Saurabh Bhardwaj | 2 | 31 | 4.92 |
| J. R. P. Gupta | 3 | 51 | 6.26 |