Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data

Abstract

Fuzzy clustering algorithms are helpful when there exists a dataset with subgroupings of points having indistinct boundaries and overlap between the clusters. Traditional methods have been extensively studied and used on real-world data, but require users to have some knowledge of the outcome a priori in order to determine how many clusters to look for. Additionally, iterative algorithms choose the optimal number of clusters based on one of several performance measures. In this study, the authors compare the performance of three algorithms (fuzzy c-means, Gustafson-Kessel, and an iterative version of Gustafson-Kessel) when clustering a traditional data set as well as real-world geophysics data that were collected from an archaeological site in Wyoming. Areas of interest in the were identified using a crisp cutoff value as well as a fuzzy α-cut to determine which provided better elimination of noise and non-relevant points. Results indicate that the α-cut method eliminates more noise than the crisp cutoff values and that the iterative version of the fuzzy clustering algorithm is able to select an optimum number of subclusters within a point set (in both the traditional and real-world data), leading to proper indication of regions of interest for further expert analysis

Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search

“…Various clustering algorithms have been proposed and designed, including K-means [51], Global K-means [52,53], K-medoids [54][55][56] and Fuzzy methods [57,58].…”

“…Gustafson and Kessel were the first to propose the Gustafson-Kessel fuzzy clustering (GK) algorithm [57,58]. This latter associates the cluster to its centroid and its covariance.…”

“…The main contribution of the approach presented in this paper is the hybridization of fuzzy and hard semi-supervised clustering algorithms tuned with ALO to distinguish a Higgs signal from a background in an unsupervised way. The clustering algorithms used in this hybridization are K-means [51], Global K-means [52,53], K-medoids [54][55][56], Fuzzy C-means [57,58], Gustafson-Kessel [57,58] and Gath-Geva [57,58] algorithms. To bring to a close the performances of this hybridization, a comparative study is done with some metaheuristic methods such as GA [26,59] and PSO [60,61] using multiple validity indexes.…”

Analysis of regional development disparities in Ukraine with fuzzy clustering technique

“…The theoretical basics on clustering methods, fuzzy clustering algorithms and their program software implementations are considered in numerous works [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. In this study, we used the fuzzy clustering approaches to identify disparities in the development of Ukrainian regions, which allow us to explore and utilize the advantages of this technique.…”

Classifying Asian dust aerosols and their columnar optical properties using fuzzy clustering

“…Furthermore, most objects do not belong to categories absolutely but may have properties consistent with more than one category. Overlapping categories based on fuzzy set theory is more natural and has been applied to a wide range of geophysical data [ Güler and Thyne , ; Miller et al ., ]. Objects on the boundaries between several clusters are not forced to fully belong to one of the clusters, but rather are assigned membership degrees between 0 and 1 indicating their partial membership or confidence degree.…”