Remotely Sensed Data Clustering Using K-Harmonic Means Algorithm and Cluster Validity Index

被引：6
|
作者：
Mahi, Habib [1 ]
Farhi, Nezha [1 ]
Labed, Kaouter [2 ]
机构：
[1] Ctr Space Tech, Earth Observat Div, Arzew, Algeria
[2] Univ USTOMB, Fac Math & Comp Sci Mohamed Boudiaf, Oran, Algeria
来源：
COMPUTER SCIENCE AND ITS APPLICATIONS, CIIA 2015 | 2015年 / 456卷
关键词：
Clustering; KHM; Cluster validity indices; Remotely sensed data; K-means; FCM;
D O I：
10.1007/978-3-319-19578-0_9
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we propose a new clustering method based on the combination of K-harmonic means (KHM) clustering algorithm and cluster validity index for remotely sensed data clustering. The KHM is essentially insensitive to the initialization of the centers. In addition, cluster validity index is introduced to determine the optimal number of clusters in the data studied. Four cluster validity indices were compared in this work namely, DB index, XB index, PBMF index, WB-index and a new index has been deduced namely, WXI. The Experimental results and comparison with both K-means (KM) and fuzzy C-means (FCM) algorithms confirm the effectiveness of the proposed methodology.
引用
收藏
页码：105 / 116
页数：12
相关论文
共 50 条
  • [21] A Novel K-harmonic Means Clustering based on Multiple Initial Centers
    Gu, Lei
    Lu, Xianling
    MECHATRONICS AND INDUSTRIAL INFORMATICS, PTS 1-4, 2013, 321-324 : 1947 - +
  • [22] Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units
    Serapiao, Adriane B. S.
    Correa, Guilherme S.
    Goncalves, Felipe B.
    Carvalho, Veronica O.
    APPLIED SOFT COMPUTING, 2016, 41 : 290 - 304
  • [23] Hybrid clustering methods based on adaptive k-harmonic means
    Chen, Jian Jiao
    Song, Anping
    Zhang, Wu
    Chen, J. J. (cjj83@shu.edu.cn), 2012, Advanced Institute of Convergence Information Technology (04) : 10 - 23
  • [24] Spectral co-clustering documents and words using fuzzy K-harmonic means
    Liu, Na
    Chen, Fei
    Lu, Mingyu
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2013, 4 (01) : 75 - 83
  • [25] Spectral co-clustering documents and words using fuzzy K-harmonic means
    Na Liu
    Fei Chen
    Mingyu Lu
    International Journal of Machine Learning and Cybernetics, 2013, 4 : 75 - 83
  • [26] An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization
    Yang, Fengqin
    Sun, Tieli
    Zhang, Changhai
    EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (06) : 9847 - 9852
  • [27] Utilization of Adaptive K-Harmonic Means Clustering and Trust Establishment in VANETs
    Jini, K. M.
    Senthilkumar, J.
    Suresh, Y.
    Mohanraj, V
    PROCEEDINGS OF SECOND INTERNATIONAL CONFERENCE ON SUSTAINABLE EXPERT SYSTEMS (ICSES 2021), 2022, 351 : 447 - 457
  • [28] On the Unification of k-Harmonic Means and Fuzzy c-Means Clustering Problems under Kernelization
    Gupta, Avisek
    Das, Swagatam
    2017 NINTH INTERNATIONAL CONFERENCE ON ADVANCES IN PATTERN RECOGNITION (ICAPR), 2017, : 386 - 391
  • [29] Data Clustering with Cluster Size Constraints Using a Modified k-means Algorithm
    Ganganath, Nuwan
    Cheng, Chi-Tsun
    Tse, Chi K.
    2014 INTERNATIONAL CONFERENCE ON CYBER-ENABLED DISTRIBUTED COMPUTING AND KNOWLEDGE DISCOVERY (CYBERC), 2014, : 158 - 161
  • [30] Soil data clustering by using K-means and fuzzy K-means algorithm
    Hot, Elma
    Popovic-Bugarin, Vesna
    2015 23RD TELECOMMUNICATIONS FORUM TELFOR (TELFOR), 2015, : 890 - 893
12345