Snipping for robust k-means clustering under component-wise contamination

被引：16
|
作者：
Farcomeni, Alessio [1 ]
机构：
[1] Univ Roma La Sapienza, Dept Publ Hlth & Infect Dis, I-00185 Rome, Italy
来源：
STATISTICS AND COMPUTING | 2014年 / 24卷 / 06期
关键词：
Clustering; k-Means; Outliers; Robustness; Snipping; Trimming;
D O I：
10.1007/s11222-013-9410-8
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
We introduce the concept of snipping, complementing that of trimming, in robust cluster analysis. An observation is snipped when some of its dimensions are discarded, but the remaining are used for clustering and estimation. Snipped k-means is performed through a probabilistic optimization algorithm which is guaranteed to converge to the global optimum. We show global robustness properties of our snipped k-means procedure. Simulations and a real data application to optical recognition of handwritten digits are used to illustrate and compare the approach.
引用
收藏
页码：907 / 919
页数：13
相关论文
共 50 条
  • [31] Mode Shape Estimation using Complex Principal Component Analysis and k-Means Clustering
    Haugdal, Hallvar
    Uhlen, Kjetil
    2019 INTERNATIONAL CONFERENCE ON SMART GRID SYNCHRONIZED MEASUREMENTS AND ANALYTICS (SGSMA), 2019,
  • [32] Stabilization of Cluster Centers over Fuzziness Control Parameter in Component-wise Fuzzy c-Means Clustering
    Das, Diptesh
    Sinha, Aniruddha
    Chakravarty, Kingshuk
    Konar, Amit
    2013 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ - IEEE 2013), 2013,
  • [33] Clustering of Image Data Using K-Means and Fuzzy K-Means
    Rahmani, Md. Khalid Imam
    Pal, Naina
    Arora, Kamiya
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2014, 5 (07) : 160 - 163
  • [34] Global k-means plus plus : an effective relaxation of the global k-means clustering algorithm
    Vardakas, Georgios
    Likas, Aristidis
    APPLIED INTELLIGENCE, 2024, 54 (19) : 8876 - 8888
  • [35] PSO Aided k-Means Clustering: Introducing Connectivity in k-Means
    Breaban, Mihaela Elena
    Luchian, Henri
    GECCO-2011: PROCEEDINGS OF THE 13TH ANNUAL GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2011, : 1227 - 1234
  • [36] Fast K-means for Large Scale Clustering
    Hu, Qinghao
    Wu, Jiaxiang
    Bai, Lu
    Zhang, Yifan
    Cheng, Jian
    CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 2017, : 2099 - 2102
  • [37] A k-means approach to clustering disease progressions
    Duc Thanh Anh Luong
    Chandola, Varun
    2017 IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI), 2017, : 268 - 274
  • [38] A Novel MapReduce Based k-Means Clustering
    Sinha, Ankita
    Jana, Prasanta K.
    PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND COMMUNICATION, 2017, 458 : 247 - 255
  • [39] Seeding on Samples for Accelerating K-Means Clustering
    Low, Jia Shun
    Ghafoori, Zahra
    Bezdek, James C.
    Leckie, Christopher
    3RD INTERNATIONAL CONFERENCE ON BIG DATA AND INTERNET OF THINGS (BDIOT 2019), 2018, : 41 - 45
  • [40] Comparison of conventional and rough K-means clustering
    Lingras, P
    Yan, R
    West, C
    ROUGH SETS, FUZZY SETS, DATA MINING, AND GRANULAR COMPUTING, 2003, 2639 : 130 - 137
12345