A novel kernel Self-Organizing Map Algorithm for Clustering

被引：0

作者：

Chen, Ning ^{[1
]}

Zhang, Hongyi ^{[2
]}

Pu, Jiexin ^{[3
]}

机构：

[1] Univ Jimei, Mech Engn Coll, Xiamen, Fujian Province, Peoples R China

[2] Xiamen Univ Technol, Elect & Elect Engn Dept, Xiamen, Peoples R China

[3] Henan Univ Sci & Technol, Elect Informat Engn Coll, Luoyang, Peoples R China

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION, VOLS 1-7, CONFERENCE PROCEEDINGS | 2009年

关键词：

self-organizing map; feature space; kernel function; energy function;

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：

Kernel Methods are algorithms that, by replacing the inner product with an appropriate positive definite function, implicitly perform a nonlinear mapping of the input data into a high-dimensional feature space. In this paper, a novel kernel SOM(self-organizing map) algorithm is proposed based on energy function for solving the disadvantage lies in lack of direct descriptions about the clusterings' centers and results in the original SOM algorithm. Furthermore, how to determine the parameters initialization is also discussed in this paper. To identify the effective of the proposed algorithm, some data are applied to test KSOM and SOM algorithm,The result of the experiments show KSOM algorithm are good performance than SOM.

引用

页码：2978 / +

页数：2

共 50 条

[31] An Improved Self-Organizing Map for Bugs Data Clustering
Ahmed, Attika
Ghazali, Rozaida
[J]. 2016 IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC CONTROL AND INTELLIGENT SYSTEMS (I2CACIS), 2016, : 135 - 140
[32] An Enhancing Dynamic Self-Organizing Map for Data Clustering
Wang, Ting
Yu, Xinghuo
Alahakoon, Damminda
Fei, Shumin
[J]. 2013 10TH IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION (ICCA), 2013, : 1324 - 1329
[33] Clustering-Based Adaptive Self-Organizing Map
Olszewski, Dominik
[J]. ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING (ICAISC 2021), PT I, 2021, 12854 : 182 - 192
[34] Self-organizing map and clustering for wastewater treatment monitoring
García, HL
González, LM
[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2004, 17 (03) : 215 - 225
[35] An extended self-organizing map (ESOM) for hierarchical clustering
Hashemi, R
Bahar, M
De Agostino, S
[J]. INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOL 1-4, PROCEEDINGS, 2005, : 2856 - 2860
[36] Visual clustering of trademarks using the self-organizing map
Hussain, M
Eakins, J
Sexton, G
[J]. IMAGE AND VIDEO RETRIEVAL, 2002, 2383 : 147 - 156
[37] An Efficient Self-Organizing Map Learning Algorithm with Winning Frequency of Neurons for Clustering Application
Chaudhary, Vikas
Ahlawat, Anil K.
Bhatia, R. S.
[J]. PROCEEDINGS OF THE 2013 3RD IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC), 2013, : 672 - 676
[38] An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm
Yang, Ping
Wang, Dan
Wei, Zhuojun
Dui, Xiaolin
Li, Tong
[J]. IEEE ACCESS, 2019, 7 : 115914 - 115925
[39] Integration of growing self-organizing map and bee colony optimization algorithm for part clustering
Kuo, R. J.
Rizki, M.
Zulvia, Ferani E.
Khasanah, A. U.
[J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2018, 120 : 251 - 265
[40] Fuzzy kernel self-organizing map neural network and the application
Lv, Q
Yu, JS
[J]. System Simulation and Scientific Computing, Vols 1 and 2, Proceedings, 2005, : 504 - 508

← 1 2 3 4 5 →