An Improved Algorithm of K-means Based on Evolutionary Computation

被引：10
|
作者：
Wang, Yunlong [1 ,2 ,3 ]
Luo, Xiong [1 ,2 ,4 ]
Zhang, Jing [1 ,2 ,3 ]
Zhao, Zhigang [1 ]
Zhang, Jun [5 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China
[2] Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China
[3] Inner Mongolia Univ Technol, Key Lab Wind Energy & Solar Energy Technol, Minist Educ, Hohhot 010051, Peoples R China
[4] Univ Sci & Technol Beijing, Shunde Grad Sch, Foshan 528399, Peoples R China
[5] North China Inst Sci & Technol, Sci & Technol Div, Beijing 101601, Peoples R China
来源：
INTELLIGENT AUTOMATION AND SOFT COMPUTING | 2020年 / 26卷 / 05期
基金：
北京市自然科学基金; 中国国家自然科学基金; 国家重点研发计划;
关键词：
Evolutionary computation; jaya algorithm; K-means; local optimum; simulated annealing; JAYA ALGORITHM; OPTIMIZATION;
D O I：
10.32604/iasc.2020.010128
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
K-means is a simple and commonly used algorithm, which is widely applied in many fields due to its fast convergence and distinctive performance. In this paper, a novel algorithm is proposed to help K-means jump out of a local optimum on the basis of several ideas from evolutionary computation, through the use of random and evolutionary processes. The experimental results show that the proposed algorithm is capable of improving the accuracy of K-means and decreasing the SSE of K-means, which indicates that the proposed algorithm can prevent K-means from falling into the local optimum to some extent.
引用
收藏
页码：961 / 971
页数：11
相关论文
共 50 条
  • [1] Evolutionary Improved Swarm-Based Hybrid K-Means Algorithm for Cluster Analysis
    Nayak, Janmenjoy
    Kanungo, D. P.
    Naik, Bighnaraj
    Behera, H. S.
    [J]. PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION TECHNOLOGIES, IC3T 2015, VOL 1, 2016, 379 : 343 - 352
  • [2] Research on k-means Clustering Algorithm An Improved k-means Clustering Algorithm
    Shi Na
    Liu Xumin
    Guan Yong
    [J]. 2010 THIRD INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY AND SECURITY INFORMATICS (IITSI 2010), 2010, : 63 - 67
  • [3] A Clustering K-means Algorithm Based on Improved PSO Algorithm
    Tan, Long
    [J]. 2015 FIFTH INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS AND NETWORK TECHNOLOGIES (CSNT2015), 2015, : 940 - 944
  • [4] Improved K-means algorithm based on density Canopy
    Zhang, Geng
    Zhang, Chengchang
    Zhang, Huayu
    [J]. KNOWLEDGE-BASED SYSTEMS, 2018, 145 : 289 - 297
  • [5] Tobacco Distribution Based on Improved K-means Algorithm
    Zheng, Bin
    Tang, Fa-zhe
    Yang, Hua-long
    [J]. PROCEEDINGS OF 2009 IEEE INTERNATIONAL CONFERENCE ON SERVICE OPERATION, LOGISTICS AND INFORMATICS, 2009, : 724 - +
  • [6] An Improved K-Means Algorithm Based on Evidence Distance
    Zhu, Ailin
    Hua, Zexi
    Shi, Yu
    Tang, Yongchuan
    Miao, Lingwei
    [J]. ENTROPY, 2021, 23 (11)
  • [7] An Improved K-Means Algorithm Based on Contour Similarity
    Zhao, Jing
    Bao, Yanke
    Li, Dongsheng
    Guan, Xinguo
    [J]. MATHEMATICS, 2024, 12 (14)
  • [8] An Improved K-means Algorithm based on Mapreduce and Grid
    Ma, Li
    Gu, Lei
    Li, Bo
    Ma, Yue
    Wang, Jin
    [J]. INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING, 2015, 8 (01): : 189 - 199
  • [9] An Improved K-Means Algorithm Based on Fuzzy Metrics
    Geng, Xinyu
    Mu, Yukun
    Mao, Senlin
    Ye, Jinchi
    Zhu, Liping
    [J]. IEEE ACCESS, 2020, 8 (08): : 217416 - 217424
  • [10] An Improved K-Means Algorithm Based on Kurtosis Test
    Wang, Tingxuan
    Gao, Junyao
    [J]. 2019 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, AUTOMATION AND CONTROL TECHNOLOGIES (AIACT 2019), 2019, 1267
12345