Publications

Our survey paper "On Clustering Validation Techniques",
by Maria Halkidi, Yannis Batistakis, Michalis Vazirgiannis,
invited to be considered for publication in the Journal of Intelligent
Information Systems (Special Issue on Scientific and Statistical
Database Management) is available.

Full paper at www.db-net.aueb.gr/mhalk/papers/validity_survey.pdf (PDF format)
Other related publications at www.db-net.aueb.gr/publications.html

ABSTRACT

Cluster analysis aims at identifying groups of similar objects and,
therefore helps to discover distribution of patterns and interesting
correlations in large data sets. It has been subject of wide research
since it arises in many application domains in engineering, business
and social sciences. Especially, in the last years the availability of
huge transactional and experimental data sets and the arising
requirements for data mining created needs for clustering algorithms
that scale and can be applied in diverse domains.

This paper introduces the fundamental concepts of clustering while it
surveys the widely known clustering algorithms in a comparative
way. Moreover, it addresses an important issue of clustering process
regarding the quality assessment of the clustering results. This is
also related to the inherent features of the data set under concern. A
review of clustering validity measures and approaches available in the
literature is presented. Furthermore, the paper illustrates the issues
that are under-addressed by the recent algorithms and gives the trends
in clustering process.

Keywords: clustering algorithms, unsupervised learning, cluster
validity, validity indices, quality assessment.