Research On Unsupervised Clustering Algorithm And Support Vector Machine And Their Application

Clustering analysis method divides data set into some groups by a certain distance or similarity measure and finds out their distributed pattern. According to the different study style, clustering method is divided into supervised and unsupervised algorithm. Classification accuracy of supervised learning algorithm is usually high, but it needs many appropriate labeled training samples that are sometimes difficultly gained, which restricts its application. Moreover sometimes in factual application, because of lacking the foreknowledge of the pattern classes, people can only use unlabeled samples, namely unsupervised classified method, but it usually gains baddish classification results. According to such situation, the paper integrates unsupervised with supervised classification algorithm, and presents a fully automatically classification method of combing K-means with SVM. The main contributions of this thesis are given as follows:(1) We discuss several unsupervised clustering algorithms, and emphatically introduce K-means and FCM algorithms, and compare and analyze their classification results for Iris, Wine and Remote sensing data set.(2) We deeply study Support Vector Machine algorithm, and propose a hybrid model of combing K-means and SVM. Experimental results for Iris data, Wine data and Remote sensing data verify the validity of our hybrid method.(3) We use Particle Swarm Optimization (PSO) and Quantum-behaved Particle Swarm Optimization (QPSO) to improve K-means algorithm, and experimental results for Iris, Wine, Breast cancer and Remote sensing data show the validity of improved algorithm.(4) We combine the improved K-means and SVM to classify original data set. Experimental results for Iris, Wine and Remote sensing data suggest the advantages of the hybrid method.