Open AccessClustering of attribute and/or relational data
Author(s) -
Anus̆ka Ferligoj,
Luka Kronegger
Publication year - 2009
Publication title -
metodološki zvezki
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.127
H-Index - 7
eISSN - 1854-0031
pISSN - 1854-0023
DOI - 10.51936/gvzj6999
Subject(s) - cluster analysis , data mining , correlation clustering , computer science , constrained clustering , conceptual clustering , fuzzy clustering , cure data clustering algorithm , relational database , canopy clustering algorithm , data stream clustering , constraint (computer aided design) , artificial intelligence , mathematics , geometry
A large class of clustering problems can be formulated as an optimizational problem in which the best clustering is searched for among all feasible clustering according to a selected criterion function. This clustering approach can be applied to a variety of very interesting clustering problems, as it is possible to adapt it to a concrete clustering problem by an appropriate specification of the criterion function and/or by the definition of the set of feasible clusterings. Both, the blockmodeling problem (clustering of the relational data) and the clustering with relational constraint problem (clustering of the attribute and relational data) can be very successfully treated by this approach. It also opens many new developments in these areas. The paired clustering approaches are applied to the Slovenian scientific collaboration data.