Generalized k-medians clustering for strings

被引:0
|
作者
Martínez-Hinarejos, CD [1 ]
Juan, A [1 ]
Casacuberta, F [1 ]
机构
[1] Univ Politecn Valencia, Inst Tecnol Informat, Dept Sistemes Informat & Computacio, Valencia 46022, Spain
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Clustering methods are used in pattern recognition to obtain natural groups from a data set in the framework Of unsupervised learning as well as for obtaining clusters of data from a known class. In sets of strings, the concept of set median string can be extended to the (set) k-medians problem. The solution of the k-medians problem can be viewed as a clustering method, where each cluster is generated by each of the k strings of that solution. A concept which is related to set median string is the (generalized) median string, which is an NP-Hard problem. However, different algorithms have been proposed to find approximations to the (generalized) median string. We propose extending the (generalized) median string problem to k strings, resulting in the generalized k-medians problem, which can also be viewed as a clustering technique. This new technique is applied to a corpus of chromosomes represented by strings and compared to the conventional k-medians technique.
引用
收藏
页码:502 / 509
页数:8
相关论文
共 50 条
  • [1] Brain Storm Optimization Algorithms with K-medians Clustering Algorithms
    Zhu, Haoyu
    Shi, Yuhui
    2015 SEVENTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI), 2015, : 107 - 110
  • [2] Attainable accuracy guarantee for the k-medians clustering in [0, 1]
    Michael Khachay
    Daniel Khachay
    Optimization Letters, 2019, 13 : 1837 - 1853
  • [3] Comparison of four initialization techniques for the K-medians clustering algorithm
    Juan, A
    Vidal, E
    ADVANCES IN PATTERN RECOGNITION, 2000, 1876 : 842 - 852
  • [4] A fast and recursive algorithm for clustering large datasets with k-medians
    Cardot, Herve
    Cenac, Peggy
    Monnez, Jean-Marie
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2012, 56 (06) : 1434 - 1449
  • [5] Attainable accuracy guarantee for the k-medians clustering in [0,1]
    Khachay, Michael
    Khachay, Daniel
    OPTIMIZATION LETTERS, 2019, 13 (08) : 1837 - 1853
  • [6] K-MEDIANS CLUSTERING BASED l1-PCA MODEL
    Lam, Shu Yan
    Choy, Siu Kai
    2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP), 2015, : 1359 - 1363
  • [7] PERFORMANCE OF JOHNSON-LINDENSTRAUSS TRANSFORM FOR k-MEANS AND k-MEDIANS CLUSTERING
    Makarychev, Konstantin
    Makarychev, Yury
    Razenshteyn, Ilya
    SIAM JOURNAL ON COMPUTING, 2023, 52 (02)
  • [8] Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering
    Makarychev, Konstantin
    Makarychev, Yury
    Razenshteyn, Ilya
    PROCEEDINGS OF THE 51ST ANNUAL ACM SIGACT SYMPOSIUM ON THEORY OF COMPUTING (STOC '19), 2019, : 1027 - 1038
  • [9] Faster algorithms for k-medians in trees
    Benkoczi, R
    Bhattacharya, B
    Chrobak, M
    Larmore, LL
    Rytter, W
    MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE 2003, PROCEEDINGS, 2003, 2747 : 218 - 227
  • [10] Random Cuts are Optimal for Explainable k-Medians
    Makarychev, Konstantin
    Shan, Liren
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,