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Projects / Programmes
Data Analysis and Combinatorial Optimization
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
(metodology)
Citations
Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
Organisations (1) , Researchers (5)
0101 Institute of Mathematics, Physics and Mechanics
| no. | Code | Name and surname | Research area | Role | Period | No. of publicationsNo. of publications |
|---|---|---|---|---|---|---|
| 1. | 01467 | Mathematics | Head | 2001 - 2003 | 994 | |
| 2. | 02017 | Mathematics | Researcher | 2003 | 293 | |
| 3. | 11410 | Computer science and informatics | Researcher | 2001 - 2003 | 152 | |
| 4. | 00213 | Mathematics | Researcher | 2001 - 2003 | 207 | |
| 5. | 03430 | Mathematics | Researcher | 2001 - 2003 | 829 |
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.