@Article{jai.2020.014944,
AUTHOR = {Samih M. Mostafa},
TITLE = {Clustering Algorithms: Taxonomy, Comparison, and Empirical Analysis in 2D  Datasets},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {2},
YEAR = {2020},
NUMBER = {4},
PAGES = {189--215},
URL = {http://www.techscience.com/jai/v2n4/41108},
ISSN = {2579-003X},
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.},
DOI = {10.32604/jai.2020.014944}
}