一种新的数据流模糊聚类方法

孙力娟; 陈小东; 韩崇; 郭剑

doi:10.11999/JEIT141415

一种新的数据流模糊聚类方法

doi: 10.11999/JEIT141415

2.
(南京邮电大学计算机学院南京 210003) ②(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)

基金项目:

国家自然科学基金(61171053, 61300239)，教育部博士点基金(20113223110002)，中国博士后科学基金(2014M551635)和江苏省博士后科研资助计划项目(1302085B)资助课题

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- 被引次数: 0
出版历程
- 收稿日期: 2014-11-05
- 修回日期: 2015-03-20
- 刊出日期: 2015-07-19

New Fuzzy-Clustering Algorithm for Data Stream

2.
(College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)

摘要

摘要: 针对数据流上的聚类任务受到时间、空间限制等问题，该文提出一种基于权值衰减的数据流模糊微簇聚类算法(WDSMC)。该算法使用改进的带权值的模糊C均值算法进行处理，并采用微簇结构和权值时间衰减结构提高聚类质量。实验表明，相对于现有的数据流加权模糊C均值聚类(SWFCM)算法和StreamKM++算法而言，WDSMC算法具有更好的聚类精度。
- 数据挖掘 /
- 数据流 /
- 模糊C均值聚类 /
- 权值衰减 /
- 微簇聚类
Abstract: There is a great challenge in the data stream clustering due to a limitation of time and space. In order to solve this problem, a new fuzzy-clustering algorithm, called Weight Decay Streaming Micro Clustering (WDSMC), is presented in this paper. The algorithm uses a reformed weighted Fuzzy C-Means (FCM) algorithm, and improves the quality of clustering by the structures of micro-clusters and weight-decay. Experimental results show that this algorithm has better accuracy than Stream Weight Fuzzy C-Means (SWFCM) and StreamKM++ algorithm.
- Data mining /
- Data stream /
- Fuzzy C-Means (FCM) /
- Weight decay /
- Micro-clustering

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参考文献(20)

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