Clustering Aluminum Smelting Potlines Using Fuzzy C-Means and K-Means Algorithms

被引：1
|
作者：
de Lima, Flavia A. N. [1 ]
de Souza, Alan M. F. [1 ]
Soares, Fabio M. [1 ]
Cardoso, Diego Lisboa [2 ]
de Oliveira, Roberto C. L. [2 ]
机构：
[1] Fed Univ Para, Postgrad Program Elect Engn, Belem, Para, Brazil
[2] Fed Univ Para, Fac Elect & Comp Engn, Belem, Para, Brazil
来源：
LIGHT METALS 2017 | 2017年
关键词：
Aluminium smelting potlines; Clusters of cells; Data patterns; Fuzzy C-Means; K-Means;
D O I：
10.1007/978-3-319-51541-0_73
中图分类号：
TF [冶金工业];
学科分类号：
0806 ;
摘要：
Aluminum smelting potlines usually have a big number of cells, producing aluminum in a continuous and complex process. Analytical monitoring is essential to increase the industries' competitive advantage, however, during their operation, some cells share similar behaviors, therefore forming clusters of cells. These clusters rely on data patterns that are usually implicit or invisible to operation, but can be found by means data analysis. In this work we present two clustering techniques (Fuzzy C-Means and K-Means) to find and cluster the cells that present similar behaviors. The benefits of clustering are mainly in the simplification of potline analysis, since a large number of cells can be summarized in one single cluster, which can provide richer but compacted information for control and modelling.
引用
收藏
页码：589 / 597
页数：9
相关论文
共 50 条
  • [41] Weighted Intuitionistic Fuzzy C-Means Clustering Algorithms
    Meenakshi Kaushal
    Q. M. Danish Lohani
    Oscar Castillo
    [J]. International Journal of Fuzzy Systems, 2024, 26 : 943 - 977
  • [42] EFFICIENT IMPLEMENTATION OF THE FUZZY C-MEANS CLUSTERING ALGORITHMS
    CANNON, RL
    DAVE, JV
    BEZDEK, JC
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1986, 8 (02) : 248 - 255
  • [43] Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy C Means and K-Means Algorithms
    Karaca, Yeliz
    Cattani, Carlo
    Moonis, Majaz
    Bayrak, Sengul
    [J]. COMPLEXITY, 2018,
  • [44] Fuzzy c-means and K-means clustering with genetic algorithm for identification of homogeneous regions of groundwater quality
    Mohammadrezapour, Omolbani
    Kisi, Ozgur
    Pourahmad, Fariba
    [J]. NEURAL COMPUTING & APPLICATIONS, 2020, 32 (08): : 3763 - 3775
  • [45] Fuzzy c-means and K-means clustering with genetic algorithm for identification of homogeneous regions of groundwater quality
    Omolbani Mohammadrezapour
    Ozgur Kisi
    Fariba Pourahmad
    [J]. Neural Computing and Applications, 2020, 32 : 3763 - 3775
  • [46] Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks
    Pickens, Adam
    Sengupta, Saptarshi
    [J]. MACHINE LEARNING AND KNOWLEDGE EXTRACTION, 2021, 3 (03): : 695 - 719
  • [47] Tumor Detection in Brain MRI Image Using Template based K-means and Fuzzy C-means Clustering Algorithm
    Ahmmed, Rasel
    Hossain, Md. Foisal
    [J]. 2016 INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND INFORMATICS (ICCCI), 2016,
  • [48] Federated c-Means and Fuzzy c-Means Clustering Algorithms for Horizontally and Vertically Partitioned Data
    Bárcena, Jose Luis Corcuera
    Marcelloni, Francesco
    Renda, Alessandro
    Bechini, Alessio
    Ducange, Pietro
    [J]. IEEE Transactions on Artificial Intelligence, 2024, 5 (12): : 6426 - 6441
  • [49] Fuzzy Clustering Using C-Means Method
    Krastev, Georgi
    Georgiev, Tsvetozar
    [J]. TEM JOURNAL-TECHNOLOGY EDUCATION MANAGEMENT INFORMATICS, 2015, 4 (02): : 144 - 148
  • [50] Clustering of Cities Based on Their Smart Performances: A Comparative Approach of Fuzzy C-Means, K-Means, and K-Medoids
    Kenger, Omer N.
    Kenger, Zulal Diri
    Ozceylan, Eren
    Mrugalska, Beata
    [J]. IEEE ACCESS, 2023, 11 : 134446 - 134459
12345