@conference {13430,
	title = {Data Clustering with a Relational Push-Pull Model},
	booktitle = {Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {189 - 194},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a new generative model for relational data in which relations between objects can have ei- ther a binding or a separating effect. For example, in a group of students separated into gender clusters, a "dating" relation would appear most frequently between the clusters, but a "roommate" relation would appear more often within clusters. In visualizing these rela- tions, one can imagine that the "dating" relation effec- tively pushes clusters apart, while the "roommate" re- lation pulls clusters into tighter formations. A unique aspect of the model is that an edge{\textquoteright}s existence is depen- dent on both the clusters to which the two connected objects belong and the features of the connected objects. We use simulated annealing to search for optimal val- ues of the unknown model parameters, where the ob- jective function is a Bayesian score derived from the generative model. Results describing the performance of the model are shown with artificial data as well as a subset of the Internet Movie Database. The results show that discovering a relation{\textquoteright}s tendency to either push or pull is critical to discovering a consistent clustering.},
	isbn = {0-7695-3033-8, 978-0-7695-3019-2},
	doi = {10.1109/ICDMW.2007.61},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4476666\&tag=1},
	author = {Anthony,Adam and desJardins, Marie}
}
