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Projects / Programmes source: ARIS

Data Analysis and Combinatorial Optimization

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Periods
January 1, 1999 - December 31, 2003
Research activity

Code Science Field Subfield
1.07.00  Natural sciences and mathematics  Computer intensive methods and applications   
1.01.00  Natural sciences and mathematics  Mathematics   

Code Science Field
P160  Natural sciences and mathematics  Statistics, operations research, programming, actuarial mathematics 
P170  Natural sciences and mathematics  Computer science, numerical analysis, systems, control 
S213  Social sciences  Social structures 
S274  Social sciences  Research methodology in science 
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Researchers (5)
no. Code Name and surname Research area Role Period No. of publications
1.  01467  PhD Vladimir Batagelj  Mathematics  Head  2001 - 2003  977 
2.  02017  PhD Matevž Bren  Mathematics  Researcher  2003  291 
3.  11410  PhD Damijana Keržič  Computer science and informatics  Researcher  2001 - 2003  119 
4.  00213  PhD Egon Zakrajšek  Mathematics  Researcher  2001 - 2003  207 
5.  03430  PhD Janez Žerovnik  Mathematics  Researcher  2001 - 2003  805 
Organisations (1)
no. Code Research organisation City Registration number No. of publications
1.  0101  Institute of Mathematics, Physics and Mechanics  Ljubljana  5055598000  20,221 
Abstract
The project is a continuation of our earlier research in data analysis and combinatorial optimization. The main topic of the research are (very) large datasets – several tens of thousands of units. For which problems there exist very efficient (subquadratic) algorithms? If not, can we find efficient approximative algorithms? How to present the results in clear and intuitive way? The main problems that we intend to study are: clustering of large datasets; data visualization; blockmodeling, partitioning/decomposition and visualization of large networks; (generalized) colorings of graphs; and dissimilarities in data analysis. The developed solutions will be implemented in software. We will continue to develop Pajek – a program for analysis and visualization of large networks.
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