Finding cohesive clusters for analyzing knowledge communities (2009)
Vasileios Kandylas, S. Phineas Upham, Lyle H. Ungar
Documents and authors can be clustered into “knowledge communities ” based on the overlap in the papers they cite. We introduce a new clustering algorithm, Streemer, which finds cohesive...
Analyzing knowledge communities using foreground and (2009)
Vasileios Kandylas, S. Phineas Upham, Lyle H. Ungar
background clusters
Projective clustering of high dimensional data (2009)
Clustering of high-dimensional data can be problematic, because the usual notions of distance or similarity break down for data in high dimensions. More specifically, it can be shown that, as the...
Winner-Take-All EM Clustering (2009)
Vasileios Kandylas, Lyle H. Ungar, Dean P. Foster
The EM algorithm is often used with mixture models to cluster data, but for efficiency reasons it is sometimes desirable to produce hard clusters. Several hard clustering limits of EM are known. For...