Data clustering is an unsupervised data analysis and data mining technique, which offers
refined and more abstract views to the inherent structure of a data set by partitioning it into a …

This work is devoted to the development of scalable and robust algorithms for data mining
and knowledge discovery problems. The main interest lies in so-called prototype-based …

This report presents the results from the research study on applying largescale data mining
methods into analysis of traffic accidents on the Finnish roads. The data sets collected from …

Introduction: Incomplete data sets containing some missing attributes is a prevailing problem
in many research areas. The reasons for the lack of missing attributes may be several; …

Typically, industrial optimization problems need to be solved in an efficient, multiobjective
and global manner, because they are often computationally expensive (as function values …

Non-uniqueness of solutions and sensitivity to erroneous data are common problems to
large-scale data clustering tasks. In order to avoid poor quality of solutions with partitioning …

Clustering or cluster analysis is an essential part of data mining, machine learning, and
pattern recognition. The most popularly applied clustering methods are partitioning-based or …

Full article: Convergence of a SOR-Weiszfeld Type Algorithm for Incomplete Data Sets Skip to
Main Content Taylor and Francis Online homepage Taylor and Francis Online homepage Access …

Tutkielman kokonaistavoite on vastata tietotulvan tuomiin haasteisiin tiedonlouhinnan
tekniikoita käyttäen. Yleisenä tutkimuskohteena on tiedonlouhinta rakenteisista …

The application of the Weiszfeld method to diff-convex (DC) problems of so-called perturbed
spatial medians are studied with a focus on clustering problems involving incomplete data …