Research on data stream clustering algorithms

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• 作者：Shifei Ding (1) (2)
  Fulin Wu (1)
  Jun Qian (1)
  Hongjie Jia (1)
  Fengxiang Jin (3)
  
  1. School of Computer Science and Technology ; China University of Mining and Technology ; Xuzhou ; 221116 ; China
  2. Key Laboratory of Intelligent Information Processing ; Institute of Computing Technology ; Chinese Academy of Science ; Beijing ; 100190 ; China
  3. College of Geomatics ; Shandong University of Science and Technology ; Qingdao ; 266590 ; China
  
• 关键词：Data mining ; Data stream ; Clustering ; Data model
• 刊名：Artificial Intelligence Review
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：43
• 期：4
• 页码：593-600
• 全文大小：136 KB
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• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Science, general
  Complexity
  
• 出版者：Springer Netherlands
• ISSN：1573-7462

文摘

Data stream is a potentially massive, continuous, rapid sequence of data information. It has aroused great concern and research upsurge in the field of data mining. Clustering is an effective tool of data mining, so data stream clustering will undoubtedly become the focus of the study in data stream mining. In view of the characteristic of the high dimension, dynamic, real-time, many effective data stream clustering algorithms have been proposed. In addition, data stream information are not deterministic and always exist outliers and contain noises, so developing effective data stream clustering algorithm is crucial. This paper reviews the development and trend of data stream clustering and analyzes typical data stream clustering algorithms proposed in recent years, such as Birch algorithm, Local Search algorithm, Stream algorithm and CluStream algorithm. We also summarize the latest research achievements in this field and introduce some new strategies to deal with outliers and noise data. At last, we put forward the focal points and difficulties of future research for data stream clustering.