An Improved Spectral Clustering Method And Its Application

With the development and progress of human society,more and more complex data are pouring into people's live.To deal with these disorderly data,clustering analysis is always the most simply and effective method.Being a kind of generalized classification method,clustering analysis could classify data automatically under certain conditions,screen and process the data.The result including not only enhance the regularity and reliability of the data,but also extract valuable information.Spectral clustering method is a kind of clustering algorithm with superior performance.It could work for the intersecting data with arbitrary shape.The original spectral clustering algorithm presents the essential features of data perfectly and could classify the original data.But when there is a cross-over between the data,the results of the algorithm are not ideal.Because the elements in the setting of the weight in this algorithm are too few,the stability of the algorithm is very unstable,and is affected easily by noise or other factors.Therefore,in view of this defect of spectral clustering algorithm,an improved spectral clustering method is mainly introduced in this paper.We consider the influence of distance properties and geometric properties between the points.Firstly,the basic concepts and theoretical basis of clustering algorithms are introduced,especially spectral clustering algorithms.The existing results and application status in spectral clustering algorithm are analyzed.And then,several key problems involved in the spectral clustering are summarized.Secondly,it focuses on the similarities and differences between spectral multiple manifold clustering algorithm and traditional spectral clustering algorithm.On the basis of considering the advantages and disadvantages of the two methods,we define a new distance metric,aiming to the construction of the similarity matrix in traditional spectral clustering algorithm.A new weight matrix combined by Euclidean distance and geodesic distance is constructed,an improved spectral clustering algorithm is proposed.Finally,we compare the clustering results and algorithm complexity of spectral clustering algorithm,spectral clustering on multiple manifolds algorithm and improved algorithm proposed in this paper.The results show that the improved algorithm proposed in this paper can deal with cross sample data,and the performance is more perfect.The clustering results here obtained are more ideal.