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
  • [21] Clustering Categorical Data:A Cluster Ensemble Approach
    何增友
    HighTechnologyLetters, 2003, (04) : 8 - 12
  • [22] A Support Based Initialization Algorithm for Categorical Data Clustering
    Kumar, Ajay
    Kumar, Shishir
    JOURNAL OF INFORMATION TECHNOLOGY RESEARCH, 2018, 11 (02) : 53 - 67
  • [23] HABOS clustering algorithm for categorical data
    Wu, Sen (wusen@manage.ustb.edu.cn), 2016, Science Press (38):
  • [24] Ensemble ROCK Methods and Ensemble SWFM Methods for Clustering of Cross Citrus Accessions Based on Mixed Numerical and Categorical Dataset
    Alvionita
    Sutikno
    Suharsono, A.
    3RD INTERNATIONAL SEMINAR ON SCIENCES SCIENCES ON PRECISION AND SUSTAINABLE AGRICULTURE (ISS-2016), 2017, 58
  • [25] Clustering algorithm for Boolean and categorical data
    Liu, H.
    Deng, H.
    Lu, S.
    Huazhong Ligong Daxue Xuebao/Journal Huazhong (Central China) University of Science and Technology, 2001, 29 (03): : 30 - 32
  • [26] Bi-level clustering of mixed categorical and numerical biomedical data
    Andreopoulos, Bill
    An, Aijun
    Wang, Xiaogang
    INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, 2006, 1 (01) : 19 - 56
  • [27] Clustering Data of Mixed Categorical and Numerical Type With Unsupervised Feature Learning
    Lam, Dao
    Wei, Mingzhen
    Wunsch, Donald
    IEEE ACCESS, 2015, 3 : 1605 - 1613
  • [28] Weighted Delta Factor Cluster Ensemble Algorithm for Categorical Data Clustering in Data Mining
    Sengottaian, Sarumathi
    Natesan, Shanthi
    Mathivanan, Sharmila
    INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, 2017, 14 (03) : 275 - 284
  • [29] Incremental clustering of mixed data based on distance hierarchy
    Hsu, Chung-Chian
    Huang, Yan-Ping
    EXPERT SYSTEMS WITH APPLICATIONS, 2008, 35 (03) : 1177 - 1185
  • [30] A k-mean clustering algorithm for mixed numeric and categorical data
    Ahmad, Amir
    Dey, Lipika
    DATA & KNOWLEDGE ENGINEERING, 2007, 63 (02) : 503 - 527
12345