Kosztyán, Zsolt Tibor and Telcs, András and Abonyi, János (2022) A multi-block clustering algorithm for high dimensional binarized sparse data. Expert Systems with Applications, 191. No. 116219. ISSN 0957-4174
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Abstract
We introduce a multidimensional multiblock clustering (MDMBC) algorithm in this paper. MDMBC can generate overlapping clusters with similar values along clusters of dimensions. The parsimonious binary vector representation of multidimensional clusters lends itself to the application of efficient meta-heuristic optimization algorithms. In this paper, a hill-climbing (HC) greedy search algorithm has been presented that can be extended by several stochastic and population-based meta-heuristic frameworks. The benefits of the algorithm are demonstrated in a bi-clustering benchmark problem and in the analysis of the Leiden higher education ranking system, which measures the scientific performance of 903 institutions along four dimensions of 20 indicators representing publication output and collaboration in different scientific fields and time periods.
| Item Type: | Article |
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| Uncontrolled Keywords: | Multidimensional clustering, High dimensional data, Ranking, Higher educational institutes |
| Subjects: | H Social Sciences / társadalomtudományok > HA Statistics / statisztika L Education / oktatás > LB Theory and practice of education / oktatás elmélete és gyakorlata > LB2300 Higher Education / felsőoktatás Q Science / természettudomány > QA Mathematics / matematika |
| Depositing User: | Dr. Zsuzsanna Banász |
| Date Deposited: | 09 Jun 2022 07:57 |
| Last Modified: | 03 Apr 2023 07:48 |
| URI: | http://real.mtak.hu/id/eprint/143626 |
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