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Projects / Programmes source: ARIS

Optimization and dissimilarities in data analysis

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Research activity

Code Science Field Subfield
1.07.02  Natural sciences and mathematics  Computer intensive methods and applications  Optimisations 

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 
Keywords
clustering, network analysis, data visualization, large datasets, dissimilarities, efficient algorithms, optimization, software, test data, Internet services
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 (2)
no. Code Name and surname Research area Role Period No. of publications
1.  01467  PhD Vladimir Batagelj  Mathematics  Head  1998 - 2000  1 
2.  02017  PhD Matevž Bren  Mathematics  Researcher  1999 - 2000  0 
Organisations (1)
no. Code Research organisation City Registration number No. of publications
1.  0101  Institute of Mathematics, Physics and Mechanics  Ljubljana  5055598000  1 
Abstract
In last years our attention is focused on analysis and visualization of large networks nad datasets. We are searching for quick (subquadratic) algorithms to perform the tasks. In visualization we are searching for new 3D representations (often based on VRML - Virtual Reality Modeling Language) of different aspects of data. In cases for which there is no quick exact algorithm we shall try to develop efficient approximative algorithms based on different heuristics. Special attention will be given to different partitioning problems on networks that allow us to reduce several problems to smaller subproblems. The use of an appropriate (dis)similarity measure is a basis of several approaches to operationalization of data analysis problems. We shall continue our research on transformations that improve the quality of (dis)similarity measures.
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