Self-adaptive Clustering Data Stream Algorithm Based on SSMC-Tree

被引：0
|
作者：
Yang, Kehua [1 ]
HeqingGao [1 ]
Chen, Lin [1 ]
Yuan, Qiong [1 ]
机构：
[1] Hunan Univ, Lab Embedded Syst & Network, Changsha 410082, Hunan, Peoples R China
来源：
PROCEEDINGS OF 2013 IEEE 4TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS) | 2012年
关键词：
Data Stream; Data Mining; Hierarchic Clustering;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Due to the data stream is real-time, fast, unlimited, one-pass, clustering data stream requires algorithms which are capable to process the data stream in the limited time and memory. In this paper, we propose a clustering algorithm based on the improved similarity search tree (SSMC-Tree), and introduce buffer, hitchhike processing and local aggregation strategy, it can adapt to different speed data stream. We adopt an outlier processing mechanism by introducing potential core-micro-cluster buffer and outlier micro-cluster buffer to process noise in the data stream. Experimental results show that our algorithm can adapt to the high-speed data stream with noise.
引用
收藏
页码：342 / 345
页数：4
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