A multi-block clustering algorithm for high dimensional binarized sparse data
نویسندگان
چکیده
We introduce a multidimensional multiblock clustering (MDMBC) algorithm in this paper. MDMBC can generate overlapping clusters with similar values along of dimensions. The parsimonious binary vector representation lends itself to the application efficient meta-heuristic optimization algorithms. In paper, hill-climbing (HC) greedy search has been presented that be extended by several stochastic and population-based frameworks. benefits are demonstrated bi-clustering benchmark problem analysis Leiden higher education ranking system, which measures scientific performance 903 institutions four dimensions 20 indicators representing publication output collaboration different fields time periods.
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ژورنال
عنوان ژورنال: Expert Systems With Applications
سال: 2022
ISSN: ['1873-6793', '0957-4174']
DOI: https://doi.org/10.1016/j.eswa.2021.116219