A Two-stage Coverage Of The Clustering Algorithm And Its Application

Cluster analysis is a kind of multivariate statistical method of sample classification and data analysis without training sample set. When we utilize this method to classify the given data, though we don't know the type of adopted sample, it will classify the samples automatically according to their similarity. The main objective in clustering analysis is to discover natural groupings of the data, so that the data within each group are relatively similar (i.e., they possess largely the same characteristics) and the observations in different groups are relatively dissimilar. A good result of clustering, on the one hand, we can grasp the each group with given data's characteristics according to their inherent nature, so it comes up to the object of a few underlying and is useful for further discussion and research to the given problem.In this thesis, we propose two-step covering cluster algorithm by the idea of covering algorithm. Meanwhile, in the process of analyzing some clustering problems, which includes some inter-relevant between character variable, We use Principal Component Analysis to overcome bad influence on stability of clustering some variables which are highly correlated. We try to promote the speed of clustering algorithm, and guarantee the valid of the clustering result, and through some analysis of examples to illustrate and examine the feasibility and validation of the putting forward method.