< Terug naar vorige pagina

Publicatie

An expressive similarity measure for relational clustering using neighbourhood trees

Boekbijdrage - Boekhoofdstuk Conferentiebijdrage

In this paper, we introduce a novel similarity measure for relational data. It is the first measure to incorporate a wide variety of types of similarity, including similarity of attributes, similarity of relational context, and proximity in a hypergraph. We experimentally evaluate how using this similarity affects the quality of clustering on very different types of datasets. The experiments demonstrate that (a) using this similarity in standard clustering methods consistently gives good results, whereas other measures work well only on datasets that match their bias; and (b) on most datasets, the novel similarity outperforms even the best among the existing ones. This is a summary of the paper accepted to Machine Learning journal (Dumancic & Blockeel, 2017).
Boek: Benelearn 2017 Extended abstracts
Pagina's: 127 - 129
Jaar van publicatie:2017
Toegankelijkheid:Open