.png){kind=icon}
返回
Clustering with proximity knowledge and relational knowledge
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2011.12.019.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this article, a proximity fuzzy framework for clustering relational data is presented, where the relationships between the entities of the data are given in terms of proximity values. We offer a comprehensive and in-depth comparison of our clustering framework with proximity relational knowledge to clustering with distance relational knowledge, such as the well known relational Fuzzy C-Means (FCM). We conclude that proximity can provide a richer description of the relationships among the data and this offers a significant advantage when realizing clustering. We further motivate clustering relational proximity data and provide both synthetic and real-world experiments to demonstrate both the usefulness and advantage offered by clustering proximity data. Finally, a case study of relational clustering is introduced where we apply proximity fuzzy clustering to the problem of clustering a set of trees derived from software requirements engineering. The relationships between trees are based on the degree of closeness in both the location of the nodes in the trees and the semantics associated with the type of connections between the nodes. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Relational clustering
Fuzzy clustering
Proximity
Knowledge representation
Software requirements
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
分享收藏Clustering and aggregation of relational data with applications to image database categorization.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Edit distance-based kernel functions for structural pattern classificationPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容