- News
- Subject and Goals
- Council and Annual Reports
- Contacts and Working Groups
- Lectures and Presentations
- Conferences and Seminars
- Publications
- Application Demos
- International Travels
- Visitors
- Past Events
- E-Synergy Login
SEARCH
MEMBERS OF RESEARCH CENTER
Research Centre
Data - Algorithms - Decision Making
Data - Algorithms - Decision Making
Established in 2005 under support of MŠMT ČR (project 1M0572)
Publications
EM cluster analysis for categorical data.
Typ:
Jornal article
Authors:
Name of journal:
Lecture Notes in Computer Science
Year:
2006
Pages:
640-648
ISSN:
0302-9743
Anotation:
Distribution mixtures with product components have been applied repeatedly to determine clusters in multivariate data. Unfortunately for categorical variables the mixture parameters are not uniquely identifiable and therefore the result of cluster analysis may become questionable. We prove that any non-degenerate discrete product mixture can be equivalently described by infinitely many different parameter sets. A unique result of cluster analysis can be guaranteed by additional constraints.