A Research of Data Stratification Algorithm based on Semi-supervised Clustering

被引：0
|
作者：
Yang, Shaobo [1 ]
Yu, Jianmin [1 ]
Liu, Yi [1 ]
机构：
[1] Beijing Inst Technol, Coll Informat & Elect, Beijing 100081, Peoples R China
来源：
PROCEEDINGS OF 2015 IEEE INTERNATIONAL CONFERENCE ON PROGRESS IN INFORMATCS AND COMPUTING (IEEE PIC) | 2015年
关键词：
Data processing; semi-supervised clustering; risk stratification; MKKZ-PCKmeans; RISK; TOOL;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A novel data stratification algorithm based on semi-supervised clustering algorithm was proposed. This proposed algorithm can automatically cluster undiagnosed people to no risk, low risk, medium risk and high risk level. Compared with traditional unsupervised clustering named K-Means algorithm and semi-supervised clustering methods that named Seeded Kmeans, COP-Kmeans and PC-Kmeans, the sensitivity of proposed method in this research, called MKKZ-PCKmeans, increased by about 11% as the results shown. This method can simply and quickly get the risk stratification of type II diabetes mellitus for the crowd who are unable to carry out the blood test. Meanwhile, this method can be used in other applications on data processing, especially in space information network.
引用
收藏
页码：196 / 200
页数：5
相关论文
共 50 条
  • [1] A semi-supervised clustering algorithm for data exploration
    Bouchachia, A
    Pedrycz, W
    [J]. FUZZY SETS AND SYSTEMS - IFSA 2003, PROCEEDINGS, 2003, 2715 : 328 - 337
  • [2] Stratification-based semi-supervised clustering algorithm for arbitrary shaped datasets
    Wang, Fei
    Li, Le
    Liu, Zhiqiang
    [J]. INFORMATION SCIENCES, 2023, 639
  • [3] Research of semi-supervised spectral clustering algorithm based on pairwise constraints
    Ding, Shifei
    Jia, Hongjie
    Zhang, Liwen
    Jin, Fengxiang
    [J]. NEURAL COMPUTING & APPLICATIONS, 2014, 24 (01): : 211 - 219
  • [4] Research of semi-supervised spectral clustering algorithm based on pairwise constraints
    Shifei Ding
    Hongjie Jia
    Liwen Zhang
    Fengxiang Jin
    [J]. Neural Computing and Applications, 2014, 24 : 211 - 219
  • [5] Research of Immune Intrusion Detection Algorithm Based on Semi-supervised Clustering
    Wang, Xiaowei
    [J]. ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, PT II, 2011, 7003 : 69 - 74
  • [6] Semi-supervised clustering algorithm based on small size of labeled data
    Leng, Mingwei
    Chen, Xiaoyun
    Cheng, Jianjun
    Li, Longjie
    [J]. FRONTIERS OF MANUFACTURING AND DESIGN SCIENCE II, PTS 1-6, 2012, 121-126 : 4675 - 4679
  • [7] Clustering Analysis of Gene Expression Data based on Semi-supervised Visual Clustering Algorithm
    Fu-lai Chung
    Shitong Wang
    Zhaohong Deng
    Chen Shu
    D. Hu
    [J]. Soft Computing, 2006, 10 : 981 - 993
  • [8] Clustering analysis of gene expression data based on semi-supervised visual clustering algorithm
    Chung, Fu-lai
    Wang, Shitong
    Deng, Zhaohong
    Shu, Chen
    Hu, D.
    [J]. SOFT COMPUTING, 2006, 10 (11) : 981 - 993
  • [9] A semi-supervised document clustering algorithm based on EM
    Rigutini, L
    Maggini, M
    [J]. 2005 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, PROCEEDINGS, 2005, : 200 - 206
  • [10] A Semi-supervised Clustering Algorithm Based on Rough Reduction
    Lin, Liandong
    Qu, Wei
    Yu, Xiang
    [J]. CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS, 2009, : 5427 - +
12345