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oai:gredos.usal.es:10366/1392242025-04-30T21:03:53Zcom_10366_138329com_10366_122682com_10366_4666com_10366_3823col_10366_139093

Hassanat, Ahmad 2019-02-05T12:03:27Z 2019-02-05T12:03:27Z 2018-09-13 ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 7 (2018) 2255-2863 http://hdl.handle.net/10366/139224 Finding the diameter of a dataset in multidimensional Euclidean space is a well-established problem, with well-known algorithms. However, most of the algorithms found in the literature do not scale well with large values of data dimension, so the time complexity grows exponentially in most cases, which makes these algorithms impractical. Therefore, we implemented 4 simple greedy algorithms to be used for approximating the diameter of a multidimensional dataset; these are based on minimum/maximum l2 norms, hill climbing search, Tabu search and Beam search approaches, respectively. The time complexity of the implemented algorithms is near-linear, as they scale near-linearly with data size and its dimensions. The results of the experiments (conducted on different machine learning data sets) prove the efficiency of the implemented algorithms and can therefore be recommended for finding the diameter to be used by different machine learning applications when needed. application/pdf eng Ediciones Universidad de Salamanca (España) Attribution-NonCommercial-NoDerivs 3.0 Unported https://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Computación Informótica Computing Information Technology Greedy Algorithms for Approximating the Diameter of Machine Learning Datasets in Multidimensional Euclidean Space: Experimental Results info:eu-repo/semantics/article