Mobile Grid Clustering Analysis And Its Application Research In Data-stream Management

In recent years,a growing number of application environment generate data streams.Such data are continuous,orderly,rapid changing,massive.How to mine useful information in data stream has attracted open attention.Clustering is an important data mining method as well as the research on applying clustering technologies to data streams.The traditional clustering technologies can not be applied to data streams directly,so it is worth researching data stream clustering algorithm.How to store such data should be researched when mining the data.The traditional database can not be adapted to data streams.It is necessary to research new data stream management system.The current research in data stream management focused on continuous query,hardly combining data stream mining with data-stream management.This paper studied the traditional methods of data mining and the current data stream cluster algorithms,the data stream cluster algorithm MGDDS based on mobile grid and density is presented.This algorithm reduces the defects of current data stream cluster algorithm and improves the result of processing the non-spherical cluster,which can handle any type of cluster and enhance the accuracy of the cluster.It analyzes the limitation of the database management system in this paper,the features of data stream management system under the data stream environment and the distinction between DSMS and DBMS.The improved structure of data stream management system is presented with referencing on the current data-stream system combining with the data-stream mining.At last,the properties of the algorithm were analyzed focusing on the actual field of data stream and the implementation process of algorithm in network intrusion detection was given,examining the commonly dataset KDDCup99 with a good result as well as authenticated the properties of algorithm.