We present a declarative framework for collective deduplication of entity references in the
presence of constraints. Constraints occur naturally in many data cleaning domains and can …

Clustering is an unsupervised process which aims to discover regularities and underlying
structures in data. Constrained clustering extends clustering in such a way that expert …

Constrained clustering is becoming an increasingly popular approach in data mining. It
offers a balance between the complexity of producing a formal definition of thematic classes …

Data clustering is a difficult problem due to the complex and heterogeneous natures of
multidimensional data. To improve clustering accuracy, we propose a scheme to capture the …

Network traffic classification is an essential component for network management and
security systems. To address the limitations of traditional port-based and payload-based …

This paper provides an overview of the current state-of-the-art on using constraints in
knowledge discovery and data mining. The use of constraints in a data mining task requires …

Pairwise constraints (PCs) composed of must-links (MLs) and cannot-links (CLs) are widely
used in many semisupervised tasks. Due to the limited number of PCs, pairwise constraint …