Survey on clustering methods: Towards fuzzy clustering for big data

Abstract

In this report, we propose to give a review of the most used clustering methods in the literature. First, we give an introduction about clustering methods, how they work and their main challenges. Second, we present the clustering methods with some comparisons including mainly the classical partitioning clustering methods like well-known k-means algorithms, Gaussian Mixture Modals and their variants, the classical hierarchical clustering methods like the agglomerative algorithm, the fuzzy clustering methods and Big data clustering methods. We present some examples of clustering algorithms comparison. Finally, we present our ideas to build a scalable and noise insensitive clustering system based on fuzzy type-2 clustering methods.