On Fuzzy Clustering of Data Streams with Concept Drift

被引：0
|
作者：
Jaworski, Maciej [1 ]
Duda, Piotr [1 ]
Pietruczuk, Lena [1 ]
机构：
[1] Czestochowa Tech Univ, Dept Comp Engn, PL-42200 Czestochowa, Poland
来源：
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT II | 2012年 / 7268卷
关键词：
IDENTIFICATION; ALGORITHMS; SYSTEMS;
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：
In the paper the clustering algorithms based on fuzzy set theory are considered. Modifications of the Fuzzy C-Means and the Possibilistic C-Means algorithms are presented, which adjust them to deal with data streams. Since data stream is of infinite size, it has to be partitioned into chunks. Simulations show that this partitioning procedure does not affect the quality of clustering results significantly. Moreover, properly chosen weights can be assigned to each data element. This modification allows the presented algorithms to handle concept drift during simulations.
引用
收藏
页码：82 / 91
页数：10
相关论文
共 50 条
  • [41] The Gradual Resampling Ensemble for mining imbalanced data streams with concept drift
    Ren, Siqi
    Liao, Bo
    Zhu, Wen
    Li, Zeng
    Liu, Wei
    Li, Keqin
    [J]. NEUROCOMPUTING, 2018, 286 : 150 - 166
  • [42] Dynamically Adjusting Diversity in Ensembles for the Classification of Data Streams with Concept Drift
    Hidalgo, Juan I. G.
    Santos, Silas G. T. C.
    Barros, Roberto S. M.
    [J]. ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 2022, 16 (02)
  • [43] An Ensemble Classifier Method for Classifying Data Streams with Recurrent Concept Drift
    Wei, Guiying
    Zhang, Tao
    Wu, Sen
    Zou, Lei
    [J]. 4TH INTERNATIONAL CONFERENCE ON AWARENESS SCIENCE AND TECHNOLOGY (ICAST 2012), 2012, : 3 - 9
  • [44] Clustering of concept-drift categorical data implementation in JAVA
    Reddy Madhavi, K.
    Vinaya Babu, A.
    Viswanadha Raju, S.
    [J]. Communications in Computer and Information Science, 2012, 270 CCIS (PART II): : 639 - 654
  • [45] A Multiscale Concept Drift Detection Method for Learning from Data Streams
    Wang, XueSong
    Kang, Qi
    Zhou, MengChu
    Yao, SiYa
    [J]. 2018 IEEE 14TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE), 2018, : 786 - 790
  • [46] SDDM: an interpretable statistical concept drift detection method for data streams
    Simona Micevska
    Ahmed Awad
    Sherif Sakr
    [J]. Journal of Intelligent Information Systems, 2021, 56 : 459 - 484
  • [47] Online Ensemble Using Adaptive Windowing for Data Streams with Concept Drift
    Sun, Yange
    Wang, Zhihai
    Liu, Haiyang
    Du, Chao
    Yuan, Jidong
    [J]. INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2016,
  • [48] SDDM: an interpretable statistical concept drift detection method for data streams
    Micevska, Simona
    Awad, Ahmed
    Sakr, Sherif
    [J]. JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 2021, 56 (03) : 459 - 484
  • [49] A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams
    Yang, Li
    Shami, Abdallah
    [J]. IEEE Internet of Things Magazine, 2021, 4 (02): : 96 - 101
  • [50] Using dynamical systems tools to detect concept drift in data streams
    da Costa, F. G.
    Rios, R. A.
    de Mello, R. F.
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2016, 60 : 39 - 50
12345