Cluster validity measurement for arbitrary shaped clusters

Authors:
Ferenc Kovács;Renáta Iváncsy
Affiliations:
Department of Automation and Applied Informatics, Budapest University of Technology and Economics, Budapest, Hungary;Department of Automation and Applied Informatics, Budapest University of Technology and Economics, Budapest, Hungary
Venue:
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Year:
2006

Citing 9
Cited 2

Applied multivariate techniques

Applied multivariate techniques
CURE: an efficient clustering algorithm for large databases

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data clustering: a review

ACM Computing Surveys (CSUR)
Data Mining Techniques: For Marketing, Sales, and Customer Support

Data Mining Techniques: For Marketing, Sales, and Customer Support
Cluster validity methods: part I

ACM SIGMOD Record
Clustering validity checking methods: part II

ACM SIGMOD Record
On Clustering Validation Techniques

Journal of Intelligent Information Systems
Clustering Validity Assessment: Finding the Optimal Partitioning of a Data Set

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Pattern Recognition, Third Edition

Pattern Recognition, Third Edition

Analyzing the role of dimension arrangement for data visualization in radviz

PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
A proposed IPC-based clustering method for exploiting expert knowledge and its application to strategic planning

Journal of Information Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

Evolutionary optimization algorithms contain, due to their heuristic inspiration, many heuristic parameters, which need to be empirically tuned for the algorithm to work most properly. This paper deals with tuning those parameters in situations when ...