Comparison of Internal Validity Indices for Fuzzy Clustering




Partitioning clustering has been one of the key components of data analytics to discover meaningful patterns in agricultural big data, driven by the increasing use of IoT-based technologies in smart farming. In partitioning clustering, the quality of clustering or performances of clustering algorithms are mostly evaluated by using the internal validity indices. In this study, the effectiveness of some widely used internal fuzzy indices are compared using the basic Fuzzy C-Means clustering algorithm. It is especially aimed to investigate changes in the effectiveness of validity indices when fuzzy data points are at different distances from the cluster centers. According to the results obtained on the simulated two-dimensional datasets, Fuzzy Silhouette, Fuzzy Hypervolume and Kwon are the most successful indices in validation of fuzzy clustering results.

Author Biography

Zeynel Cebeci, Cukurova University

Div. of Biometry and Genetics Faculty of Agriculture




How to Cite

Cebeci, Z. (2019). Comparison of Internal Validity Indices for Fuzzy Clustering. Journal of Agricultural Informatics, 10(2).



Journal of Agricultural Informatics