Evaluation of hierarchical clustering algorithms for document datasets

@inproceedings{citeulike:104932, abstract = {Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In particular, hierarchical clustering solutions provide a view of the data at different levels of granularity, making them ideal for people to visualize and interactively explore large document collections.In this paper we evaluate different partitional and agglomerative approaches for hierarchical clustering. Our experimental evaluation showed that partitional algorithms always lead to better clustering solutions than agglomerative algorithms, which suggests that partitional clustering algorithms are well-suited for clustering large document datasets due to not only their relatively low computational requirements, but also comparable or even better clustering performance. We present a new class of clustering algorithms called constrained agglomerative algorithms that combine the features of both partitional and agglomerative algorithms. Our experimental results showed that they consistently lead to better hierarchical solutions than agglomerative or partitional algorithms alone}, author = {Zhao, Ying and Karypis, George }, booktitle = {CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management}, citeulike-article-id = {104932}, doi = {http://dx.doi.org/10.1145/584792.584877}, isbn = {1581134924}, pages = {515--524}, posted-at = {2008-01-15 13:18:59}, priority = {2}, publisher = {ACM Press}, title = {Evaluation of hierarchical clustering algorithms for document datasets}, url = {http://dx.doi.org/10.1145/584792.584877}, year = {2002} }

TY - CONF ID - citeulike:104932 L3 - citeulike-article-id:104932 TI - Evaluation of hierarchical clustering algorithms for document datasets T2 - CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management SP - 515 EP - 524 PB - ACM Press SN - 1581134924 N2 - Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In particular, hierarchical clustering solutions provide a view of the data at different levels of granularity, making them ideal for people to visualize and interactively explore large document collections.In this paper we evaluate different partitional and agglomerative approaches for hierarchical clustering. Our experimental evaluation showed that partitional algorithms always lead to better clustering solutions than agglomerative algorithms, which suggests that partitional clustering algorithms are well-suited for clustering large document datasets due to not only their relatively low computational requirements, but also comparable or even better clustering performance. We present a new class of clustering algorithms called constrained agglomerative algorithms that combine the features of both partitional and agglomerative algorithms. Our experimental results showed that they consistently lead to better hierarchical solutions than agglomerative or partitional algorithms alone AU - Zhao, Ying AU - Karypis, George PY - 2002/// UR - http://dx.doi.org/10.1145/584792.584877 ER -