STUDY OF THE FUZZY K -MEANS METHOD USING PYTHON PROGRAMMING LANGUAGE
Keywords:
Key words: Fuzzy c - means, k-means algorithm , Python, Fuzzy clustering, Data mining.Abstract
Abstract. Fuzzy K-Means clustering is an extension of the traditional K-Means
algorithm that allows for soft clustering, where each data point is assigned a degree of
membership to each cluster rather than being assigned to a single cluster. This article
introduces the Fuzzy K-Means clustering algorithm and demonstrates its
implementation in Python using the scikit-learn library. We explain the basic concepts
of Fuzzy K-Means clustering, including the fuzzy partition matrix and the fuzziness
parameter, and how it differs from traditional K-Means clustering. We demonstrate
how to preprocess data, choose the optimal number of clusters, and visualize the results
of Fuzzy K-Means clustering. We also discuss the advantages and disadvantages of
Fuzzy K-Means clustering and compare it to other clustering algorithms. Finally, we
provide some tips and tricks for improving the performance of Fuzzy K-Means
clustering in Python.
