Landscape of clustering algorithms

@inproceedings{citeulike:1319027, 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.}, author = {Jain, A. K. and Topchy, A. and Law, M. H. C. and Buhmann, J. M. }, citeulike-article-id = {1319027}, doi = {http://dx.doi.org/10.1109/ICPR.2004.1334073}, journal = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on}, keywords = {clustering}, pages = {260--263 Vol.1}, posted-at = {2007-12-02 12:42:10}, priority = {2}, title = {Landscape of clustering algorithms}, url = {http://dx.doi.org/10.1109/ICPR.2004.1334073}, volume = {1}, year = {2004} }

TY - CONF ID - citeulike:1319027 L3 - citeulike-article-id:1319027 TI - Landscape of clustering algorithms JF - Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on VL - 1 SP - 260 EP - 263 Vol.1 N2 - 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. KW - clustering AU - Jain, AK AU - Topchy, A AU - Law, MHC AU - Buhmann, JM PY - 2004/// UR - http://dx.doi.org/10.1109/ICPR.2004.1334073 ER -