北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2018, Vol. 41 ›› Issue (4): 29-36.doi: 10.13190/j.jbupt.2018-026

• 论文 • 上一篇    下一篇

增量式模糊C有序均值聚类算法

刘永利, 郭呈怡, 王恒达, 晁浩   

  1. 河南理工大学 计算机科学与技术学院, 河南 焦作 454000
  • 收稿日期:2018-01-26 出版日期:2018-08-28 发布日期:2018-10-09
  • 作者简介:刘永利(1980-),男,副教授,硕士生导师,E-mail:yongli.buaa@gmail.com.
  • 基金资助:
    河南省高等学校青年骨干教师项目(2015GGJS-068);河南省科技攻关计划项目(172102210279);河南省高校基本科研业务费专项资金项目(NSFRF1616)

Incremental Fuzzy C-Ordered Means Clustering

LIU Yong-li, GUO Cheng-yi, WANG Heng-da, CHAO Hao   

  1. School of Computer Science and Technology, Henan Polytechnic University, Henan Jiaozuo 454000, China
  • Received:2018-01-26 Online:2018-08-28 Published:2018-10-09

摘要: 针对传统聚类算法难以处理大规模数据和对噪声数据敏感等问题,基于模糊C有序均值聚类算法(FCOM),结合single-pass和online增量架构,分别提出了single-pass模糊C有序均值聚类算法(SPFCOM)和online模糊C有序均值聚类算法(OFCOM).SPFCOM和OFCOM算法首先对FCOM算法加权,然后以数据块为单位对数据集合进行增量式处理.实验结果表明,相较于对比算法,SPFCOM和OFCOM算法在聚类准确率方面得到了提高,还具有更强的鲁棒性.

关键词: 模糊聚类, 增量聚类, 鲁棒性

Abstract: Because traditional clustering algorithms are difficult to deal with large-scale data and sensitive to noise data, based on the Fuzzy C-ordered-means clustering (FCOM) algorithm, we propose a single-pass fuzzy C-ordered clustering algorithm, named SPFCOM, and an online fuzzy C-ordered clustering algorithm, named OFCOM, by combining single-pass and online incremental architectures respectively. These two algorithms weight the FCOM algorithm, and incrementally process the large-scale data chunk by chunk. Experimental results show that, compared with other similar prominent algorithms, the SPFCOM and OFCOM algorithms can achieve higher accuracy and better robustness.

Key words: fuzzy clustering, incremental clustering, robustness

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