A comprehensive comparative study of clustering-based unsupervised defect prediction models

被引：0
|
作者：
Xu, Zhou [1 ,2 ]
Li, Li [3 ]
Yan, Meng [1 ,2 ]
Liu, Jin [4 ]
Luo, Xiapu [5 ]
Grundy, John [3 ]
Zhang, Yifeng [4 ]
Zhang, Xiaohong [1 ,2 ]
机构：
[1] Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, China
[2] School of Big Data and Software Engineering, Chongqing University, Chongqing, China
[3] Faculty of Information Technology, Monash University, Australia
[4] School of Computer Science, Wuhan University, Wuhan, China
[5] Department of Computing, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
来源：
Journal of Systems and Software | 2021年 / 172卷
关键词：
Data Analytics;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [1] A comprehensive comparative study of clustering-based unsupervised defect prediction models
    Xu, Zhou
    Li, Li
    Yan, Meng
    Liu, Jin
    Luo, Xiapu
    Grundy, John
    Zhang, Yifeng
    Zhang, Xiaohong
    JOURNAL OF SYSTEMS AND SOFTWARE, 2021, 172
  • [2] An Experimental Study on Unsupervised Clustering-based Feature Selection Methods
    Covoes, Thiago F.
    Hruschka, Eduardo R.
    2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, 2009, : 993 - 1000
  • [3] Comparative Analysis of Offshore Wind Power Prediction Models and Clustering-Based Daily Output Classification
    Ou, Zhongxi
    Lan, Wei
    Zhang, Liang
    Tong, Zhu
    Liu, Dundun
    Liu, Zhaoxi
    2024 IEEE 2ND INTERNATIONAL CONFERENCE ON POWER SCIENCE AND TECHNOLOGY, ICPST 2024, 2024, : 1482 - 1487
  • [4] A clustering-based method for unsupervised intrusion detections
    Jiang, SY
    Song, XY
    Wang, H
    Han, JJ
    Li, QH
    PATTERN RECOGNITION LETTERS, 2006, 27 (07) : 802 - 810
  • [5] Towards Distribution Clustering-Based Deep LSTM Models for RUL Prediction
    Sayah, Mohamed
    Guebli, Djillali
    Zerhouni, Noureddine
    Al Masry, Zeina
    2020 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-BESANCON 2020), 2020, : 253 - 256
  • [6] Research and Implementation of Unsupervised Clustering-Based Intrusion Detection
    Luo Min
    WuhanUniversityJournalofNaturalSciences, 2003, (03) : 803 - 807
  • [7] An improved unsupervised clustering-based intrusion detection method
    Hai, YJ
    Wu, Y
    Wang, GY
    Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005, 2005, 5812 : 52 - 60
  • [8] A Mixed Unsupervised Clustering-based Intrusion Detection Model
    Zhang, Cuixiao
    Zhang, Guobing
    Sun, Shanshan
    THIRD INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING, 2009, : 426 - 428
  • [9] A Hybrid Unsupervised Clustering-Based Anomaly Detection Method
    Guo Pu
    Lijuan Wang
    Jun Shen
    Fang Dong
    TsinghuaScienceandTechnology, 2021, 26 (02) : 146 - 153
  • [10] A Clustering-Based Unsupervised Approach to Anomaly Intrusion Detection
    Nikolova, Evgeniya
    Jecheva, Veselina
    PROCEEDINGS OF THE 2ND INTERNATIONAL SYMPOSIUM ON COMPUTER, COMMUNICATION, CONTROL AND AUTOMATION, 2013, 68 : 202 - 205
12345