Support vector clustering transforms the data into a high dimensional feature space, where a decision function is computed. In the original space, the function outlines the bounda...
We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suit...
Gerd Stumme, Rafik Taouil, Yves Bastide, Nicolas P...
Frequent itemset mining is a classic problem in data mining. It is a non-supervised process which concerns in finding frequent patterns (or itemsets) hidden in large volumes of d...
Adriano Veloso, Wagner Meira Jr., Srinivasan Parth...
A prerequisite for leveraging the vast amount of data available on the Web is Entity Resolution, i.e., the process of identifying and linking data that describe the same real-worl...
George Papadakis, Ekaterini Ioannou, Claudia Niede...
We introduce a robust and efficient framework called CLUMP (CLustering Using Multiple Prototypes) for unsupervised discovery of structure in data. CLUMP relies on finding multip...