A Comparitive Analaysis of Fuzzy Particle Swarm Optimization with SOM and EM Algorithms

Abstract

Clustering is a kind of unsupervised learning, the process of dividing a given data set into groups according to the similarity of a given data set, and similarity is performed according to distance. Some researchers have developed some data clustering algorithms, others have implemented new algorithms, and some have studied existing data and compared other data clustering algorithms. Here are some previous studies that considered the impact of several factors on the performance of specific data clustering algorithms and compared the results. However, this study is different from algorithms and factor analysis; this article aims to study and compare functional weighted fuzzy particle clustering optimization with self-configuration mapping and expectation maximized clustering algorithms. All of these algorithms, depending on the size of the data, the number of clusters, the type of data set, and the type of software used for the comparison. Some conclusions drawn belong to the performance, quality and accuracy of the above clustering algorithm.