Combining multiple clusterings using similarity graph
Multiple clusterings are produced for various needs and reasons in both distributed and local environments. Combining multiple clusterings into a final clustering which has better overall quality has gained importance recently. It is also expected that the final clustering is novel, robust, and scal...
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| Veröffentlicht in: | Pattern recognition 2011-03, Vol.44 (3), p.694-703 |
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| container_title | Pattern recognition |
| container_volume | 44 |
| creator | Mimaroglu, Selim Erdil, Ertunc |
| description | Multiple clusterings are produced for various needs and reasons in both distributed and local environments. Combining multiple clusterings into a final clustering which has better overall quality has gained importance recently. It is also expected that the final clustering is novel, robust, and scalable. In order to solve this challenging problem we introduce a new graph-based method. Our method uses the evidence accumulated in the previously obtained clusterings, and produces a very good quality final clustering. The number of clusters in the final clustering is obtained automatically; this is another important advantage of our technique. Experimental test results on real and synthetically generated data sets demonstrate the effectiveness of our new method. |
| doi_str_mv | 10.1016/j.patcog.2010.09.008 |
| format | Article |
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Experimental test results on real and synthetically generated data sets demonstrate the effectiveness of our new method.</description><subject>Applied sciences</subject><subject>Cluster ensemble</subject><subject>Clustering</subject><subject>Clusters</subject><subject>Combining clustering partitions</subject><subject>Evidence accumulation</subject><subject>Exact sciences and technology</subject><subject>Graphs</subject><subject>Information, signal and communications theory</subject><subject>Mutual information</subject><subject>Pattern recognition</subject><subject>Robust clustering</subject><subject>Signal and communications theory</subject><subject>Signal representation. 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| ispartof | Pattern recognition, 2011-03, Vol.44 (3), p.694-703 |
| issn | 0031-3203 1873-5142 |
| language | eng |
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| source | Access via ScienceDirect (Elsevier) |
| subjects | Applied sciences Cluster ensemble Clustering Clusters Combining clustering partitions Evidence accumulation Exact sciences and technology Graphs Information, signal and communications theory Mutual information Pattern recognition Robust clustering Signal and communications theory Signal representation. Spectral analysis Signal, noise Similarity Telecommunications and information theory |
| title | Combining multiple clusterings using similarity graph |
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