Research On Algorithm And Application Of Dynamic Grid-based Clustering Over Data Stream

Recently, with the rapidly development of communication technology and sensor network, people faced large volume of data which is beyond the storage in database, these data is high-speed, continuous, dynamic, variable and infinity. We can visit them only once or limited times, and the storage is dynamic and synoptic. To dealing with it, the data stream model has been wildly concerned by researchers.As the extension of traditional clustering, data stream clustering has been one of the most important directions of data stream research. In this paper, Firstly, the traditional clustering methods have been analyzed, It finds that methods based density can discover clusters of arbitrary shape but the running time is horrible and it can't find clusters in the data sets which have different distribute. While methods based grid have high running speed, but the quality of the results is not good. Secondly, on the basic of researching and improving the existing traditional clustering methods, we have presented a new dynamic grid-based clustering algorithm which major works are summarized as follows: improving the method of density calculating, setting parameter automatically and clustering orders. Lastly experiment results show the correctness and effectiveness of algorithm.It's an interesting attempt to detect network anomaly with data stream clustering. The network brings us not only a large amount of convenience, but also inconvenience. The network is often destructive by various intrusion technique and measures. Nevertheless the traditional anomaly intrusion detection technique couldn't deal with the more and more complex attacks in the field of expansibility and adaptability, we need use other scopes of technology. Recently, clustering analytical method has been paid more attention since it can discover some unknown patterns and update the rules of anomaly intrusion detection in real-time. This paper establishes an anomaly intrusion detection system which is based on clustering analytical method and is built on Snort system. Finally it proves this system is effective by the experiment results.