Fast Artificial Bee Colony for Clustering

被引:0
|
作者
Girsang, Abba Suganda [1 ]
Muliono, Yohan [2 ]
Fanny, Fanny [2 ]
机构
[1] Bina Nusantara Univ, Comp Sci Dept, BINUS Grad Program, Comp Sci, Jakarta 11480, Indonesia
[2] Bina Nusantara Univ, Sch Comp Sci, Comp Sci Dept, Jakarta 11480, Indonesia
来源
关键词
artificial bee colony; clustering; fast ABC; redundant process;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Artificial Bee Colony (ABC) is one of good heuristic intelligent algorithm to solve optimization problem including clustering. Generally, the heuristic algorithm will take the high computation time to solve optimization problem. Likewise, ABC also consumes too much time to solve clustering problem. This paper intends solving clustering problem using ABC with focusing reduction computation time called FABCC. This idea proposes detecting the pattern of redundant process then compacting it to effective process to diminish the computation process. There are five data sets to be used to prove the performance of FABCC. The results shows that FABCC is effective to prune the duration process up to 46.58 %.
引用
收藏
页码:211 / 219
页数:9
相关论文
共 50 条
  • [31] Fast artificial bee colony and its application to stereo correspondence
    Phuc Nguyen Hong
    Ahn, Chang Wook
    EXPERT SYSTEMS WITH APPLICATIONS, 2016, 45 : 460 - 470
  • [32] A ranking paired based artificial bee colony algorithm for data clustering
    Xu, Haiping
    Dong, Zhengshan
    Xu, Meiqin
    Lin, Geng
    INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS, 2022, 16 (04) : 389 - 398
  • [33] An artificial bee colony algorithm for mixture model-based clustering
    Culos, Anthony E.
    Andrews, Jeffrey L.
    Afshari, Hamid
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2022, 51 (10) : 5658 - 5669
  • [34] A Novel Artificial Bee Colony Based Clustering Algorithm for Categorical Data
    Ji, Jinchao
    Pang, Wei
    Zheng, Yanlin
    Wang, Zhe
    Ma, Zhiqiang
    PLOS ONE, 2015, 10 (05):
  • [35] Clustering mixed numeric and categorical data with artificial bee colony strategy
    Ji, Jinchao
    Chen, Yongbing
    Feng, Guozhong
    Zhao, Xiaowei
    He, Fei
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2019, 36 (02) : 1521 - 1530
  • [36] A Feature Weighting Based Artificial Bee Colony Algorithm for Data Clustering
    Reisi, Manijeh
    Moradi, Parham
    Abdollahpouri, Alireza
    2016 EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE TECHNOLOGY (IKT), 2016, : 134 - 138
  • [37] Spatial clustering algorithm with obstacles constraints based on artificial bee colony
    Sun, Li-ping
    Luo, Yong-long
    Ding, Xin-tao
    Chen, Fu-long
    Computer Modelling and New Technologies, 2014, 18 (10): : 324 - 328
  • [38] Hyperspectral Image Clustering Method Based on Artificial Bee Colony Algorithm
    Sun, Xu
    Yang, Lina
    Zhang, Bing
    Gao, Lianru
    Zhang, Liang
    2013 SIXTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI), 2013, : 106 - 109
  • [39] A Comparative Analysis of Enhanced Artificial Bee Colony Algorithms for Data Clustering
    Krishnamoorthi, M.
    Natarajan, A. M.
    2013 INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND INFORMATICS, 2013,
  • [40] A Clustering Particle Based Artificial Bee Colony Algorithm for Dynamic Environment
    Biswas, Subhodip
    Bose, Digbalay
    Kundu, Souvik
    SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING, (SEMCCO 2012), 2012, 7677 : 151 - 159