[1]陈爱国,王士同.基于极大熵的知识迁移模糊聚类算法[J].智能系统学报,2017,12(1):95-103.[doi:10.11992/tis.201602003]
CHEN Aiguo,WANG Shitong.A maximum entropy-based knowledge transfer fuzzy clustering algorithm[J].CAAI Transactions on Intelligent Systems,2017,12(1):95-103.[doi:10.11992/tis.201602003]
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CHEN Aiguo,WANG Shitong.A maximum entropy-based knowledge transfer fuzzy clustering algorithm[J].CAAI Transactions on Intelligent Systems,2017,12(1):95-103.[doi:10.11992/tis.201602003]
基于极大熵的知识迁移模糊聚类算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
12
期数:
2017年第1期
页码:
95-103
栏目:
学术论文—机器学习
出版日期:
2017-02-25
- Title:
- A maximum entropy-based knowledge transfer fuzzy clustering algorithm
- Keywords:
- knowledge transfer; maximum entropy; clustering algorithms; maximum entropy clustering; fuzzy clustering
- 分类号:
- TP274
- 摘要:
- 针对传统的聚类算法在样本数据量不足或样本受到污染情况下的聚类性能下降问题，在经典的极大熵聚类算法（MEKTFCA）的基础上，提出了一种新的融合历史聚类中心点和历史隶属度这两种知识的基于极大熵的知识迁移模糊聚类算法。该算法通过学习由源域总结出来的有益历史聚类中心和历史隶属度知识来指导数据量不足或受污染的目标域数据的聚类任务，从而提高了聚类性能。通过一组模拟数据集和两组真实数据集构造的迁移场景上的实验，证明了该算法的有效性。
- Abstract:
- To address the issue of clustering performance degradation when traditional clustering algorithms are applied to insufficient and/or noisy data, a maximum entropy-based knowledge transfer fuzzy clustering algorithm is proposed. This improves the classical maximum entropy clustering algorithm for target domains by leveraging two kinds of knowledge from the source domain, i.e., historical clustering centers and historical degree of membership, into the objective function proposed for clustering insufficient and/or noisy target data. The effectiveness of the proposed algorithm is demonstrated by experiments on several synthetic and two real datasets.
- 参考文献/References:
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备注/Memo
收稿日期:2016-2-4;改回日期:。
基金项目:国家自然科学基金项目(61272210)； 江苏省自然科学基金项目(BK20130155)；
作者简介:陈爱国,男,1975年生,博士研究生,主要研究方向为模式识别与机器学习;王士同,男,1964年生,教授,博士生导师,中国离散数学学会常务理事,中国机器学习学会常务理事,主要研究方向为人工智能、模式识别和生物信息。发表学术论文近百篇,其中被SCI、EI检索50余篇。
通讯作者:陈爱国.E-mail:agchen@jiangnan.edu.cn.
更新日期/Last Update:
1900-01-01