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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jain, A.K. Topchy, A. Law, M.H.C. Buhmann, J.M. |
| Copyright Year | 2004 |
| Description | Author affiliation: Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA (Jain, A.K.; Topchy, A.; Law, M.H.C.) |
| Abstract | Numerous clustering algorithms, their taxonomies and evaluation studies are available in the literature. Despite the diversity of different clustering algorithms, solutions delivered by these algorithms exhibit many commonalities. An analysis of the similarity and properties of clustering objective functions is necessary from the operational/user perspective. We revisit conventional categorization of clustering algorithms and attempt to relate them according to the partitions they produce. We empirically study the similarity of clustering solutions obtained by many traditional as well as relatively recent clustering algorithms on a number of real-world data sets. Sammon's mapping and a complete-link clustering of the inter-clustering dissimilarity values are performed to detect a meaningful grouping of the objective functions. We find that only a small number of clustering algorithms are sufficient to represent a large spectrum of clustering criteria. For example, interesting groups of clustering algorithms are centered around the graph partitioning, linkage-based and Gaussian mixture model based algorithms. |
| Starting Page | 260 |
| Ending Page | 263 |
| File Size | 319489 |
| Page Count | 4 |
| File Format | |
| ISBN | 0769521282 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2004.1334073 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-08-26 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Clustering algorithms Partitioning algorithms Taxonomy Algorithm design and analysis Computer science Data analysis Data structures Guidelines Cost function Maximum likelihood detection |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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