A HYBRID CLUSTERING ALGORITHM COMBINING CLOUD MODEL IWO AND K-MEANS

被引：17
|
作者：
Pan, Guo [1 ]
Li, Kenli [1 ]
Ouyang, Aijia [1 ]
Zhou, Xu [1 ]
Xu, Yuming [1 ]
机构：
[1] Hunan Univ, Coll Informat Sci & Engn, Changsha 410082, Hunan, Peoples R China
来源：
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 2014年 / 28卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Cloud model; invasive weed optimization (IWO); K-means; clustering; hybrid algorithm; WEED OPTIMIZATION ALGORITHM; GENETIC ALGORITHM; DESIGN;
D O I：
10.1142/S0218001414500153
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In order to overcome the drawbacks of the K-means (KM) for clustering problems such as excessively depending on the initial guess values and easily getting into local optimum, a clustering algorithm of invasive weed optimization (IWO) and KM based on the cloud model has been proposed in the paper. The so-called cloud model IWO (CMIWO) is adopted to direct the search of KM algorithm to ensure that the population has a definite evolution direction in the iterative process, thus improving the performance of CMIWO K-means (CMIWOKM) algorithm in terms of convergence speed, computing precision and algorithm robustness. The experimental results show that the proposed algorithm has such advantages as higher accuracy, faster constringency, and stronger stability.
引用
收藏
页数：19
相关论文
共 50 条
  • [21] Improved Algorithm for the k-means Clustering
    Zhang, Sheng
    Wang, Shouqiang
    PROCEEDINGS OF THE 10TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA 2012), 2012, : 4717 - 4720
  • [22] Adaptive K-Means clustering algorithm
    Chen, Hailin
    Wu, Xiuqing
    Hu, Junhua
    MIPPR 2007: PATTERN RECOGNITION AND COMPUTER VISION, 2007, 6788
  • [23] CENTAURUS: A Cloud Service for K-means Clustering
    Golubovic, Nevena
    Gill, Angad
    Krintz, Chandra
    Wolski, Rich
    2017 IEEE 15TH INTL CONF ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING, 15TH INTL CONF ON PERVASIVE INTELLIGENCE AND COMPUTING, 3RD INTL CONF ON BIG DATA INTELLIGENCE AND COMPUTING AND CYBER SCIENCE AND TECHNOLOGY CONGRESS(DASC/PICOM/DATACOM/CYBERSCI, 2017, : 1135 - 1142
  • [24] k*-means:: A new generalized k-means clustering algorithm
    Cheung, YM
    PATTERN RECOGNITION LETTERS, 2003, 24 (15) : 2883 - 2893
  • [25] K*-Means: An Effective and Efficient K-means Clustering Algorithm
    Qi, Jianpeng
    Yu, Yanwei
    Wang, Lihong
    Liu, Jinglei
    PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCES ON BIG DATA AND CLOUD COMPUTING (BDCLOUD 2016) SOCIAL COMPUTING AND NETWORKING (SOCIALCOM 2016) SUSTAINABLE COMPUTING AND COMMUNICATIONS (SUSTAINCOM 2016) (BDCLOUD-SOCIALCOM-SUSTAINCOM 2016), 2016, : 242 - 249
  • [26] An Improved K-Means Clustering Algorithm Based on Semantic Model
    Liu, Zhe
    Bao, Jianmin
    Ding, Fei
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND ELECTRICAL ENGINEERING 2018 (ICITEE '18), 2018,
  • [27] Ferrographic image segmentation by the method combining k-means clustering and watershed algorithm
    Wang, Jing-Qiu
    Zhang, Long
    Wang, Xiao-Lei
    Zhongguo Kuangye Daxue Xuebao/Journal of China University of Mining and Technology, 2013, 42 (05): : 866 - 872
  • [28] Hybrid Clustering: Combining K-Means and Interval valued data-type Hierarchical Clustering
    Galdino, Sergio Mario Lins
    da Silva, Jornandes Dias
    ACTA POLYTECHNICA HUNGARICA, 2024, 21 (09) : 175 - 186
  • [29] A Hybrid K-Means Hierarchical Algorithm for Natural Disaster Mitigation Clustering
    Prasetyadi, Abdurrakhman
    Nugroho, Budi
    Tohari, Adrin
    JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY-MALAYSIA, 2022, 21 (02): : 175 - 200
  • [30] A Hybrid Model for Anomalies Detection in AMI System Combining K-means Clustering and Deep Neural Network
    Maamar, Assia
    Benahmed, Khelifa
    CMC-COMPUTERS MATERIALS & CONTINUA, 2019, 60 (01): : 15 - 39
12345