KK-means Clustering Method Improved Based-on Minimum Cost Spanning Tree And Its Applications In Seismic Data

Data Mining,which extracts useful information from large data sets or databases,is a rising cross contents of Statistics,Machine Learning ,Database,Pattern Recognition,Artificial Intelligence,etc are involved.Clustering is an important area for research in Data Mining,and also an important method in data partition or data grouping. Clustering has been widely used in commerce market analysis,biology Web classification and so on. Up to now,clustering algorithms are mainly consisted of five kinds;partition algorithm,hierarchical algorithm,density-based algorithm,grid-based algorithm and model-based algorithm. And there still exists many problems in these clustering algorithms , such as symbolic attributes,efficiency,selection of initial values,sensitivity of input sequence,optimal solutions dependence on input parameter and so on .This paper researches the selection of initial data in partition algorithm. The main work is as follows:First,the basic principles of clustering are introduced and the basic data types in clustering are described in general.Second,after all clustering algorithms are described in brief,the partition algorithm which is related to this paper is proposed,and the method of selecting primary centers is also proposed.Last,the partition algorithm which has been improved is proposed ,meanwhile,the basic idea of Minimum Cost Spanning tree is presented which it is KK-means. Further,the feasibility of this algorithm is proved by experiments.