Open Access
ARTICLE
Clustering Algorithms: Taxonomy, Comparison, and Empirical Analysis in 2D Datasets
Samih M. Mostafa1,2,*
1 Computer Science-Mathematics Department, Faculty of Science, South Valley University, Qena, 83523, Egypt
2 Fellow of the Academy of Scientific Research and Technology (ASRT), Egypt
* Corresponding Author: Samih M. Mostafa. Email:
Journal on Artificial Intelligence 2020, 2(4), 189-215. https://doi.org/10.32604/jai.2020.014944
Received 29 October 2020; Accepted 03 December 2020; Issue published 31 December 2020
Abstract
Because of the abundance of clustering methods, comparing between
methods and determining which method is proper for a given dataset is crucial.
Especially, the availability of huge experimental datasets and transactional and
the emerging requirements for data mining and the like needs badly for
clustering algorithms that can be applied in various domains. This paper presents
essential notions of clustering and offers an overview of the significant features
of the most common representative clustering algorithms of clustering categories
presented in a comparative way. More specifically the study is based on the
numerical type of the data that the algorithm supports, the shape of the clusters,
and complexity. The experiments were done using nine clustering algorithms
representing the common clustering categories on eight 2D clustered datasets
differ in the clusters’ shapes and density of the data points. Furthermore, the
comparison was done from the point of view seven performance measures.
Keywords
Cite This Article
S. M. Mostafa, "Clustering algorithms: taxonomy, comparison, and empirical analysis in 2d datasets,"
Journal on Artificial Intelligence, vol. 2, no.4, pp. 189–215, 2020. https://doi.org/10.32604/jai.2020.014944