检索结果-内蒙古大学图书馆

Enhancing point symmetry-based distance for data clustering

SOFT COMPUTING 2018年第2期22卷 409-436页

作者： Saha, Sriparna Indian Inst Technol Patna Dept Comp Sci & Engn Patna Bihar India

In this paper, at first a new point symmetry-based similarity measurement is proposed which satisfies the closure and the symmetry properties of any distance function. The different desirable properties of the new distance are elaborately explained. Thereafter a new clustering algorithm based on the search capability of genetic algorithm is developed where the newly developed point symmetry-based distance is used for cluster assignment. The allocation of points to different clusters is performed in such a way that the closure property is satisfied. The proposed GA with newly developed point symmetry distance based (GAnPS) clustering algorithm is capable of determining different symmetrical shaped clusters having any sizes or convexities. The effectiveness of the proposed GAnPS clustering technique in identifying the proper partitioning is shown for twenty-one data sets having various characteristics. Performance of GAnPS is compared with existing symmetry-based genetic clustering technique, GAPS, three popular and well-known clustering techniques, K-means, expectation maximization and average linkage algorithm. In a part of the paper, the utility of the proposed clustering technique is shown for partitioning a remote sensing satellite image. The last part of the paper deals with the development of some automatic clustering techniques using the newly proposed symmetry-based distance.

关键词： clustering Symmetry Point symmetry-based distance genetic algorithm based clustering

来源：评论

学校读者我要写书评

暂无评论

genetic algorithm and Simulated Annealing based Approaches to Categorical Data clustering

Genetic Algorithm and Simulated Annealing based Approaches t...

引用

IEEE Region 10 Colloquium/3rd International Conference on Industrial and Information Systems

作者： Saha, Indrajit Mukhopadhyay, Anirban Tech Acad Dept Informat Technol Adisaptagram 712121 W Bengal India Univ Kalyani Dept Comp Sci & Engn Kalyani W Bengal India

ISBN: (纸本)9781424428052

Categorical data clustering has been gaining significant attention from researchers, because most of the real life data sets are categorical in nature. In contrast to numerical domain, no natural ordering can be found among the elements of a categorical domain. Hence no inherent distance measure, like the Euclidean distance, would work to compute the distance between two categorical objects. In this article, genetic algorithm and simulated annealing based categorical data clustering algorithm has been proposed. The performance of the proposed algorithm has been compared with that of different well known categorical data clustering algorithms and demonstrated for a variety of artificial and real life categorical data sets.

关键词： genetic algorithm based clustering Simulated Annealing based clustering K-medoids algorithm Minkowski Score Wilcoxons rank sum test

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：