[1]唐丹[],张正军[],王俐莉[]. 基于改进的近邻传播聚类算法的Gap统计研究[J].计算机技术与发展,2017,27(01):182-185.
　TANG Dan[],ZHANG Zheng-jun[],WANG Li-li[]. Study on Gap Statistic Based on Modified Affinity Propagation Clustering[J].,2017,27(01):182-185.
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 基于改进的近邻传播聚类算法的Gap统计研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年01期
页码:
182-185
栏目:
应用开发研究
出版日期:
2017-01-10

文章信息/Info

Title:
 Study on Gap Statistic Based on Modified Affinity Propagation Clustering
文章编号:
1673-629X（2017)01-0182-04
作者:
 唐丹[1]; 张正军[1]; 王俐莉[2]
 1.南京理工大学 理学院 统计与金融数学系；2.海军指挥学院科研部
Author(s):
 TANG Dan[1]; ZHANG Zheng-jun[1]; WANG Li-li[2]
关键词:
 ; 聚类分析; 近邻传播聚类; 偏向参数; K-means算法; Gap统计
Keywords:
 ; cluster analysis; affinity propagation clustering preference; K-means algorithm; Gap statistic
分类号:
TP301.6
文献标志码:
A
摘要:
 由于K-means算法初始聚类中心的选取具有随机性,聚类结果可能不稳定,导致Gap统计估计的聚类数也可能不稳定。针对这些不足,提出一种改进的近邻传播算法-mAP。该算法考察数据的全局分布特性,不同的点赋予不同的P值。在Gap统计中用mAP算法代替K-means算法,提出基于mAP的Gap统计mAPGap。 mAP能在较短的时间内得到较好的聚类效果,而且不需要预先设定初始聚类中心,聚类结果更稳定。实验结果表明,mAPGap在估计聚类数的稳定性和聚类精度上都优于原Gap。
Abstract:
 Due to the randomness of choosing the initial clustering of K-means method,it may cause the instability of clustering results and then lead to that of clustering numbers which are estimated by Gap statistic. Taking consideration of those disadvantages,an modified AP clustering ( mAP) is presented which utilizes the global distribution to give different P to different points. mAP method is put forward to substitute the K-means in Gap statistic named mAPGap. mAP method has more stable clustering center because the initial clustering center and numbers are not needed in advance and it can get better clustering in short time. The experimental results demonstrate mAPGap is superior to Gap in clustering stability and accuracy.

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更新日期/Last Update: 2017-04-05