Clustering has always been a powerful tool in knowledge discovery. Traditionally
unsupervised, it received renewed attention when it was shown to produce better results …

Clustering has always been a powerful tool in knowledge discovery. Traditionally
unsupervised, it has received renewed attention recently as it has shown to produce better …

Clustering has always been an exploratory but critical step in the knowledge discovery
process. Often unsupervised, the clustering task received a huge interest when reinforced by …

Weighted K-Means (WKM) algorithms are increasingly important with the increase of data
dimension. WKM faces an initialization problem that is more complicated than K-Means' …

Active learning for semi-supervised clustering allows algorithms to solicit a domain expert to
provide side information as instances constraints, for example a set of labeled instances …

To address cold start problem by utilizing only rating information, this paper proposes an
incremental group-specific framework for recommender systems. Firstly, a decoupled …

This paper describes a new semi-supervised clustering algorithm as part of a more general
framework of interactive exploratory clustering, that favors the exploration of possible …

This paper proposes a new semi-supervised clustering framework to represent and integrate
quantitative preferences on attributes. A new metric learning algorithm is derived that …

Both the instance level knowledge and the attribute level knowledge can improve clustering
quality, but how to effectively utilize both of them is an essential problem to solve. This paper …

Perhaps more than ever, new economic and enterprise needs have increased the interest
and utility of the methods of the grou** process based on the theory of uncertainty. We …