基于区间2-型模糊度量的粗糙<em>K</em>-means聚类算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201803008

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Abstract

The rough k-means algorithm and its derivatives focus on the description of data objects in uncertain boundary areas. However, the influence of imbalanced sizes between clusters on the clustering result is ignored. The interval type-2 fuzzy measure is introduced in this paper for measuring the boundary objects and an improved rough K-means clustering algorithm is developed. Firstly, the membership degree interval of the boundary object is calculated according to the data distribution of clusters and thus the spatial distribution of clusters is described. Then, the data sample size of the cluster is taken into account to adaptively adjust the influence coefficient of boundary objects on overlapping clusters. The experimental results on both synthetic and UCI datasets show that the adverse impact of the boundary objects on the means iterative calculations of small sample size clusters is mitigated and the clustering accuracy is improved.

Key words： Rough Clustering K-means Interval Type-2 Fuzzy Measure Rough Sets

Received: 03 June 2017

ZTFLH:

TP 18

Fund:

Supported by National Natural Science Foundation of China(No.61403184,61105082), Major Program of Natural Science Foundation of Jiangsu Higher Education Institutions of China(No.17KJA120001), Qing Lan Project of Jiangsu Province(No.QL2016), Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD), Project of National Center for International Joint Research on E-Business Information Processing(No.2013B01035), “1311 Talent Plan” of Nanjing University of Posts and Telecommunications(No.NY2013)

Corresponding Authors: MA Fumin, Ph.D., associate professor. Her research interests include intelligent information processing and intelligent manufacturing system.

About author:: LU Ruiqiang, master student. His research interests include information processing and data mining.ZHANG Tengfei, Ph.D., professor. His research interests include inte-lligent information processing and big data analysis

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Cite this article:

LU Ruiqiang,MA Fumin,ZHANG Tengfei. Interval Type-2 Fuzzy Measure Based Rough K-means Clustering[J]. , 2018, 31(3): 265-274.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201803008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I3/265

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