ESOM: An algorithm to evolve self-organizing maps from on-line data streams

被引：45

作者：

Deng, D ^{[1
]}

Kasabov, N ^{[1
]}

机构：

[1] Univ Otago, Dept Informat Sci, Dunedin, New Zealand

来源：

IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL VI | 2000年

关键词：

D O I：

10.1109/IJCNN.2000.859364

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An algorithm of evolving self-organizing map (ESOM) is proposed as a dynamic version of the Kohonen self-organizing map, where network structure is evolved in an on-line adaptive mode. Experiments have been carried out on some benchmark data sets as well as on macroeconomic data. Results show that ESOM is a good tool for clustering, data analysis, and visualisation.

引用

页码：3 / 8

页数：6

共 16 条

[1]

[Anonymous], METHODOLOGIES CONCEP

[2]

BLACKMORE J, 1993, P IEEE INT C NEUR NE, V1, P450

[3]

DEBOECK G, 1999, INVESTMENT MAPS EMER, P373

[4]

DENG D, 1999, P ICONIP ANZIIS ANNE

[5]

*EUR CENTR BANK, 1999, EUR CENTR BANK MONTL

[6] GROWING CELL STRUCTURES - A SELF-ORGANIZING NETWORK FOR UNSUPERVISED AND SUPERVISED LEARNING [J].

FRITZKE, B .

NEURAL NETWORKS, 1994, 7 (09) :1441-1460

[7] LEARNING-PROCESSES IN NEURAL NETWORKS [J].

HESKES, TM ;

KAPPEN, B .

PHYSICAL REVIEW A, 1991, 44 (04) :2718-2726

[8]

KASABOV N, 2000, IN PRESS FUTURE DIRE

[9]

Kasabov N, 1998, J ADV COMPUTATIONAL, V2, P1

[10]

KASKI S, 1997, COMPUTING MANAGEMENT, V82

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