Akademik Veri Yönetim Sistemi
Sakarya University Journal of Computer and Information Sciences, cilt.3, sa.1, ss.11-27, 2020 (Hakemli Dergi)
In exploratory data analysis and machine learning, partitioning
clustering is a frequently used unsupervised learning technique for
finding the meaningful patterns in numeric datasets. Clustering aims to
identify and classify the objects or the cases in datasets in practice.
The clustering quality or the performance of a clustering algorithm is
generally evaluated by using the internal validity indices. In this
study, an R package named 'fcvalid' is introduced for validation of
fuzzy and possibilistic clustering results. The package implements a
broad collection of the internal indices which have been proposed to
validate the results of fuzzy clustering algorithms. Additionally, the
options to compute the generalized and extended versions of the fuzzy
internal indices for validation of the possibilistic clustering are also
included in the package.