| 基于模糊聚类的数据流概念漂移检测算法 |
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| 引用本文： | 陈小东,孙力娟,韩崇,郭剑.基于模糊聚类的数据流概念漂移检测算法[J].计算机科学,2016,43(4):219-223, 251. |
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| 作者姓名： | 陈小东 孙力娟 韩崇 郭剑 |
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| 作者单位： | 南京邮电大学计算机学院 南京210003,南京邮电大学计算机学院 南京210003;南京邮电大学江苏省无线传感网高技术研究重点实验室 南京210003,南京邮电大学计算机学院 南京210003,南京邮电大学计算机学院 南京210003;南京邮电大学江苏省无线传感网高技术研究重点实验室 南京210003 |
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| 基金项目： | 本文受国家自然科学基金(61171053,9),教育部博士点基金(20113223110002),中国博士后科学基金(2014M551635),江苏省博士后科研资助 |
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| 摘 要： | 针对数据流中可能出现的概念漂移现象,采用改进的FCM算法进行模糊聚类,提出在大小可变的滑动窗口中通过度量相邻窗口之间的差异性来判断是否发生了概念漂移,并给出了相应的处理方法。实验表明该算法能够有效地检测出数据流中的概念漂移现象,具有很好的聚类效果和很高的时间效率。
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| 关 键 词： | 概念漂移 数据流 模糊聚类 可变滑动窗口 |
| 收稿时间： | 4/8/2015 12:00:00 AM |
| 修稿时间： | 2015/6/15 0:00:00 |
Detecting Concept Drift of Data Stream Based on Fuzzy Clustering |
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| CHEN Xiao-dong,SUN Li-juan,HAN Chong and GUO Jian.Detecting Concept Drift of Data Stream Based on Fuzzy Clustering[J].Computer Science,2016,43(4):219-223, 251. |
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| Authors: | CHEN Xiao-dong SUN Li-juan HAN Chong and GUO Jian |
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| Affiliation: | College of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210003,China,College of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks,Nanjing University of Posts and Telecommunications,Nanjing 210003,China,College of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210003,China and College of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks,Nanjing University of Posts and Telecommunications,Nanjing 210003,China |
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| Abstract: | The phenomena of concept drift may occur in data stream,and how to detect it is very important in many applications.We used the improved version of FCM algorithm to cluster data in variable siding window,and measured the difference between adjacent windows to determine whether concept drift occurs.The result shows that our algorithm can detect concept drift in data stream effectively,and has great performance in clustering quality and time. |
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| Keywords: | Concept drift Data stream Fuzzy clustering Variable sliding window |
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