区间值模糊软集的信息测度及其聚类算法

doi:10.11772/j.issn.1001-9081.2015.08.2350

计算机应用 ›› 2015, Vol. 35 ›› Issue (8): 2350-2354.DOI: 10.11772/j.issn.1001-9081.2015.08.2350

区间值模糊软集的信息测度及其聚类算法

彭新东, 杨勇

西北师范大学计算机科学与工程学院, 兰州 730070

收稿日期:2015-02-12 修回日期:2015-03-18 出版日期:2015-08-10 发布日期:2015-08-14
通讯作者: 杨勇(1967-),男,甘肃兰州人,教授,博士,主要研究方向:软计算,yangzt@nwnu.edu.cn
作者简介:彭新东(1990-),男,江西九江人,硕士研究生,主要研究方向:智能决策、软计算。
基金资助:
国家自然科学基金资助项目(61163036)。

Information measures for interval-valued fuzzy soft sets and their clustering algorithm

PENG Xindong, YANG Yong

College of Computer Science and Engineering, Northwest Normal University, Lanzhou Gansu 730070, China

Received:2015-02-12 Revised:2015-03-18 Online:2015-08-10 Published:2015-08-14

摘要/Abstract

摘要：

针对区间值模糊软集信息测度难以精确定义的问题,提出了区间值模糊软集的距离测度、相似度、熵、包含度、子集度的公理化定义,给出了区间值模糊软集的信息测度公式,并讨论了它们的转换关系。然后提出了一个基于相似度的聚类算法,该算法结合区间值模糊软集的特性,着重对给出评价对象的具有相似知识水平的专家进行聚类,同时讨论了算法的计算复杂度。最后通过实例说明该算法能有效地处理专家聚类问题。

关键词: 区间值模糊软集, 信息测度, 相似度, 聚类算法, 计算复杂度

Abstract:

Focusing on the precise definition of information measures for interval-valued fuzzy soft sets, the distance measure, the similarity measure, the entropy measure, the inclusion measure, and the subsethood measure of interval-valued fuzzy soft sets were introduced. A series of formulae of information measures were presented, and their transformation relationships were discussed. Then, combining the characteristics of interval-valued fuzzy soft sets, a clustering algorithm based on similarity measure was explored. It emphasized the clustering of similar level knowledge of experts who gave the evaluation of objects. Meanwhile, the computational complexity of the algorithm was discussed. Finally, a practical example was given to prove that the proposed algorithm can effectively handle the clustering problem of experts.

Key words: interval-valued fuzzy soft set, information measure, similarity measure, clustering algorithm, computational complexity

中图分类号:

彭新东, 杨勇. 区间值模糊软集的信息测度及其聚类算法[J]. 计算机应用, 2015, 35(8): 2350-2354.

PENG Xindong, YANG Yong. Information measures for interval-valued fuzzy soft sets and their clustering algorithm[J]. Journal of Computer Applications, 2015, 35(8): 2350-2354.

参考文献

[1] MOLODTSOV D A. Soft set theory — first results [J]. Computers and Mathematics with Applications, 1999, 37(4/5): 19-31.
[2] MAJI P K, BISWAS R, ROY A R. Fuzzy soft sets [J]. Journal of Fuzzy Mathematics, 2001, 9(3): 589-602.
[3] GUI H, ZHOU H. Evaluation of Agent coalition based on fuzzy soft set theory [J]. Journal of Computer Applications, 2014, 34(11): 3250-3253. (桂海霞,周华平.基于模糊软集理论的Agent联盟评价[J].计算机应用,2014,34(11):3250-3253.
[4] ATHAR K. Distance and similarity measures for soft sets [J]. New Mathematics and Natural Computation, 2010, 6(3): 321-324.
[5] YANG W. New similarity measures for soft sets and their application [J]. Fuzzy Information and Engineering, 2013, 5(1): 19-25.
[6] ŞAHIN R, KÜÇÜK A. On similarity and entropy of neutrosophic soft sets [J]. Journal of Intelligent and Fuzzy Systems, 2014, 27(5): 2417-2430.
[7] LIU Z, QIN K, PEI Z. Similarity measure and entropy of fuzzy soft sets [J]. The Scientific World Journal, 2014, 2014: 161607.
[8] YANG Y, TAN X, MENG C. The multi-fuzzy soft set and its application in decision making [J].Applied Mathematical Modelling, 2013, 37(7): 4915-4923.
[9] YANG Y, PENG X, CHEN H, et al. A decision making approach based on bipolar multi-fuzzy soft set [J]. Journal of Intelligent and Fuzzy Systems, 2014, 27(4): 1861-1872.
[10] WANG C, QU A. Entropy, similarity measure and distance measure of vague soft sets and their relations [J]. Information Sciences, 2013, 244: 92-106.
[11] MAJUMDAR P, SAMANTA S K. Decision making based on similarity measure of vague soft set [J]. Journal of Intelligent and Fuzzy Systems, 2013, 24(3): 637-646.
[12] JIANG Y, TANG Y, LIU H, et al. Entropy on intuitionistic fuzzy soft sets and on interval-valued fuzzy soft sets [J]. Information Sciences: an International Journal, 2013, 240: 95-114.
[13] MAJI P K, BISWAS R, ROY A R. Intuitionistic fuzzy soft sets [J]. Journal of Fuzzy Mathematics, 2001, 9(3): 677-692.
[14] YANG X, LIN T Y, YANG J, et al. Combination of interval-valued fuzzy set and soft set [J]. Computers and Mathematics with Applications, 2009, 58(3): 521-527.
[15] XU Z, CHEN J, WU J. Clustering algorithm for intuitionistic fuzzy sets [J]. Information Sciences, 2008, 178(19): 3775-3790.
[16] ZADEH L A. The concept of a linguistic variable and its application to approximate reasoning I [J]. Information Sciences, 1975, 8(3): 199-249.

区间值模糊软集的信息测度及其聚类算法

Information measures for interval-valued fuzzy soft sets and their clustering algorithm

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	张成, 万源, 强浩鹏. 基于知识蒸馏的深度无监督离散跨模态哈希[J]. 计算机应用, 2021, 41(9): 2523-2531.
[2]	陈俊, 何庆. 基于余弦相似度的改进蝴蝶优化算法[J]. 计算机应用, 2021, 41(9): 2668-2677.
[3]	吴悦, 雒江涛, 刘锐, 胡钟尹. 基于感知哈希和切块的视频相似度检测方法[J]. 计算机应用, 2021, 41(7): 2070-2075.
[4]	张豪, 朱睿, 宋栿尧, 方鹏, 夏秀峰. 距离-关键字相似度约束的双色反k近邻查询方法[J]. 计算机应用, 2021, 41(6): 1686-1693.
[5]	朱怡, 宁振虎, 周艺华. 基于视觉特征的仿冒域名轻量级检测技术[J]. 计算机应用, 2020, 40(8): 2279-2285.
[6]	赵小虎, 赵成龙. 基于多特征语义匹配的知识库问答系统[J]. 计算机应用, 2020, 40(7): 1873-1878.
[7]	孙建军, 徐岩. 基于加权改进模糊C均值聚类的欠定混合矩阵估计[J]. 计算机应用, 2020, 40(6): 1769-1773.
[8]	韦伟, 李小娟. 基于相似论文增广的深度学习专利质量评估[J]. 计算机应用, 2020, 40(4): 966-971.
[9]	丁辉, 李丽宏, 原钢. 融合GMS与VCS+GC-RANSAC的图像配准算法[J]. 计算机应用, 2020, 40(4): 1138-1143.
[10]	吕一可, 徐凯, 黄振强. 基于面积划分的轨迹相似性度量方法[J]. 计算机应用, 2020, 40(2): 578-583.
[11]	吕亚丽, 苗钧重, 胡玮昕. 基于标签进行度量学习的图半监督学习算法[J]. 计算机应用, 2020, 40(12): 3430-3436.
[12]	赵昕晨, 杨楠. 基于头部姿态分析的摄像头视线追踪系统优化[J]. 计算机应用, 2020, 40(11): 3295-3299.
[13]	花超, 王庚润, 陈雷. 基于低通滤波模型的行人再识别算法[J]. 计算机应用, 2020, 40(11): 3314-3319.
[14]	沈学利, 李子健, 赫辰皓. 基于评分填充与信任信息的混合推荐算法[J]. 计算机应用, 2020, 40(10): 2789-2794.
[15]	李功丽, 李钰, 张恩, 尹天宇. 面向用户隐私保护的高效基因比对方案[J]. 计算机应用, 2020, 40(1): 136-142.