硕士生导师
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学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
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Hierarchical cluster ensemble model based on knowledge granulation
影响因子：8.139
DOI码：10.1016/j.knosys.2015.10.006
所属单位：西南交通大学
发表刊物：KNOWLEDGE-BASED SYSTEMS
刊物所在地：NETHERLANDS
关键字：Cluster ensemble Granular computing Rough sets
摘要：Cluster ensemble has been shown to be very effective in unsupervised classification learning by generating a large pool of different clustering solutions and then combining them into a final decision. However, the task of it becomes more difficult due to the inherent complexities among base cluster results, such as uncertainty, vagueness and overlapping. Granular computing is one of the fastest growing information-processing paradigms in the domain of computational intelligence and human-centric systems. As the core part of granular computing, the rough set theory dealing with inexact, uncertain, or vague information, has been widely applied in machine learning and knowledge discovery related areas in recent years. From these perspectives, in this paper, a hierarchical cluster ensemble model based on knowledge granulation is proposed with the attempt to provide a new way to deal with the cluster ensemble problem together with ensemble learning application of the knowledge granulation. A novel rough distance is introduced to measure the dissimilarity between base partitions and the notion of knowledge granulation is improved to measure the agglomeration degree of a given granule. Furthermore, a novel objective function for cluster ensembles is defined and the corresponding inferences are made. A hierarchical cluster ensemble algorithm based on knowledge granulation is designed. Experimental results on real-world data sets demonstrate the effectiveness for better cluster ensemble of the proposed method.
合写作者：Hongjun Wang, Hamido Fujita
第一作者：Jie Hu
论文类型：学术论文
通讯作者：李天瑞
论文编号：20155101701533
学科门类：工学
一级学科：计算机科学与技术
卷号：Volume 91
期号：January 2016
页面范围：Pages 179-188
ISSN号：0950-7051
是否译文：否
发表时间：2015-10-16