원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.8 No.1
2015.02
pp.55-62
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
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 computational 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.
목차
Abstract
1. Introduction
2. Related Work
3. MSF Model Introduction
4. MSFS Algorithm
4.1. Related Concepts
4.2. The Specific Process of the Algorithm
4.3. Performance Analysis
5. Experimental Results
5.1. Real Data Sets
5.2. Synthetic Data Sets
6. Discussion and Conclusions
Acknowledgments
References
1. Introduction
2. Related Work
3. MSF Model Introduction
4. MSFS Algorithm
4.1. Related Concepts
4.2. The Specific Process of the Algorithm
4.3. Performance Analysis
5. Experimental Results
5.1. Real Data Sets
5.2. Synthetic Data Sets
6. Discussion and Conclusions
Acknowledgments
References
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자료제공 : 네이버학술정보