Incremental clustering algorithm of mixed numerical and categorical data based on clustering ensemble

被引：0
|
作者：
Li, Tao-Ying [1 ]
Chen, Yan [1 ]
Zhang, Jin-Song [1 ]
Qin, Sheng-Jun [1 ]
机构：
[1] Transportation Management College, Dalian Maritime University, Dalian 116026, China
来源：
Kongzhi yu Juece/Control and Decision | 2012年 / 27卷 / 04期
关键词：
903.1 Information Sources and Analysis - 921.6 Numerical Methods;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Traditional clustering methods have disadvantages of unsteadiness, randomness and low accuracy for classifying mixed numerical and categorical data. Therefore, the incremental clustering algorithm of mixed numerical and categorical data based on clustering ensemble is proposed, which adopts the results of several clustering to replace that of single clustering and modifies the design of threshold. The experiment results show that the improved algorithm has higher stability and accuracy by using the characters of existing data, and possess better effectiveness.
引用
收藏
页码：603 / 608
相关论文
共 50 条
  • [1] Incremental Clustering for Categorical Data Using Clustering Ensemble
    Li Taoying
    Chne Yan
    Qu Lili
    Mu Xiangwei
    PROCEEDINGS OF THE 29TH CHINESE CONTROL CONFERENCE, 2010, : 2519 - 2524
  • [2] A Genetic Algorithm Based Ensemble Approach for Categorical Data Clustering
    Goswami, Jyoti Prokash
    Mahanta, Anjana Kakoti
    2015 ANNUAL IEEE INDIA CONFERENCE (INDICON), 2015,
  • [3] A CLUSTERING ALGORITHM FOR MIXED NUMERIC AND CATEGORICAL DATA
    Ohn Mar San
    Van-Nam Huynh
    Yoshiteru Nakamori
    JournalofSystemsScienceandComplexity, 2003, (04) : 562 - 571
  • [4] Fuzzy Clustering Ensemble Algorithm for Partitioning Categorical Data
    Li, Taoying
    Chen, Yan
    2009 INTERNATIONAL CONFERENCE ON BUSINESS INTELLIGENCE AND FINANCIAL ENGINEERING, PROCEEDINGS, 2009, : 170 - 174
  • [5] Density-based clustering algorithm for numerical and categorical data with mixed distance measure methods
    Chen, Jin-Yin
    He, Hui-Hao
    Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2015, 32 (08): : 993 - 1002
  • [6] Clustering mixed numerical and categorical data with missing values
    Dinh, Duy-Tai
    Huynh, Van-Nam
    Sriboonchitta, Songsak
    INFORMATION SCIENCES, 2021, 571 : 418 - 442
  • [7] Performances of parallel clustering algorithm for categorical and mixed data
    Hai, NTM
    Susumu, H
    PARALLEL AND DISTRIBUTED COMPUTING: APPLICATIONS AND TECHNOLOGIES, PROCEEDINGS, 2004, 3320 : 252 - 256
  • [8] Categorical Data Clustering Based on Cluster Ensemble Process
    Veeraiah, D.
    Vasumathi, D.
    PROCEEDINGS OF THE INTERNATIONAL CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGY, ICICT 2015, VOL 2, 2016, 439 : 101 - 111
  • [9] Ensemble based rough fuzzy clustering for categorical data
    Saha, Indrajit
    Sarkar, Jnanendra Prasad
    Maulik, Ujjwal
    KNOWLEDGE-BASED SYSTEMS, 2015, 77 : 114 - 127
  • [10] Fast Density Clustering Algorithm for Numerical Data and Categorical Data
    Chen Jinyin
    He Huihao
    Chen Jungan
    Yu Shanqing
    Shi Zhaoxia
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2017, 2017
12345