Study of Data Stream Clustering Based on MSF

Yingmei Li, Min Li, Jingbo Shao, Gaoyang Wang
2015 International Journal of Database Theory and Application  
Nowadays with the rapid development of wireless sensor networks, and network traffic monitoring, stream data gradually becomes one of the most popular data models. Stream data is different from the traditional static data. Clustering analysis is an important technology for data mining, so that many researchers pay their attention to the clustering of stream data. In this paper, MSFS algorithm is proposed. By means of the experimental verification analysis, based on biologically inspired
more » ... ional model, higher clustering purity on both the real dataset and the simulation datasets existence is demonstrated for the proposed algorithm. In other words, the cluster result of MSFS algorithm is advantageous over previous method. 56 Copyright ⓒ 2015 SERSC the method on synthetic and real life data sets are presented. Finally, we discuss the advantages of the approach and conclude this article. R is the two-dimensional Cartesian virtual space, we will deploy the agent to this space and make it move according to the rules of flocking model. MSFS can produce better clustering effect than DenStream algorithm in experimental comparison. When experiments is performed based on real data sets, MSFS algorithm achieves higher clustering purity. What's more, MSFS algorithm is more outstanding when it deals with the data whith some noise. However, because the parameters are predefined, proposed algorithm has high parameter sensitivity. In the future, this issue will be concerned and its solution is going to be proposed.
doi:10.14257/ijdta.2015.8.1.07 fatcat:x2zh2mszofbe7fog7dogpzmt6y