We consider the problem of quantizing data generated from disparate sources, e.g. subjects performing actions with different styles, movies with particular genre bias, various con...
Ekaterina Taralova, Fernando DelaTorre, Martial He...
Clustering with constraints is an emerging area of data mining research. However, most work assumes that the constraints are given as one large batch. In this paper we explore the...
Optimizing energy consumption has become a major concern in designing economical clusters. Scheduling precedence-constrained parallel tasks on clusters is challenging because of h...
Ziliang Zong, Adam Manzanares, Brian Stinar, Xiao ...
Clustering performance can often be greatly improved by
leveraging side information. In this paper, we consider constrained
clustering with pairwise constraints, which specify
s...
Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data in...
Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan ...