An Improved Fuzzyc-Means Clustering Algorithm Based on Shadowed Sets and PSO

Abstract

To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect.

Migrating Virtual Machine in Heterogen and Homogen Cloud Data Center with Efficient Electricity Consumption Using Fuzzy C-meams

“…In this experiment, we have used 3, 4, or 5 clusters to find out the optimal variance of the cluster member that suitable in this case. Moreover, the weighting value is set to 2 because its value should be more than 1 (Weight > 1) [22]. Then, the maximum iteration is set to 50, which means that the process will be running up to 50 times.…”

“…Moreover, represent the weight or hyper-parameter, this value should fulfill W > 1.0. The bigger W value which used in FCM, the fuzzier the cluster will be at the end of the process [22]. 5.…”

Improved Quantum Particle Swarm Optimization for Mangroves Classification

“…Researchers found that the human learning process has great uncertainty which is very similar to the quantum behaviour of particle, so each individual can be described as a particle in quantum space. In recent years, a series of papers have focused on the application of QPSO, such as financial forecasting [3], sensor array [4], clinical disease diagnoses [5], classification and clustering [6], fuel management optimization [7], feature selection [8], and other areas [9,10]. But traditional QPSO is easy to fall into local optimum value and the convergence rate is slow.…”

A Novel Multilayer Data Clustering Framework based on Feature Selection and Modified K-Means Algorithm

“…Their result indicates better performance compared with other state-of-the-art frameworks. In [9], Hu's group proposed a novel unsupervised possibilistic C-Means clustering to improve the efficiency of possibilistic c-means clustering (PCM) algorithm. A novel and robust clustering algorithm named as the weighted possibilistic c-means clustering (WPCC) algorithm is proposed to estimate the positions of centers of PCM accurately to serve for the coming clustering process.…”