Self-organizing-map-based metamodeling for massive text data exploration

Kin Keung Lai; Lean Yu; Ligang Zhou; Shouyang Wang

doi:10.1007/11759966_187

Self-organizing-map-based metamodeling for massive text data exploration

Kin Keung Lai, Lean Yu, Ligang Zhou, Shouyang Wang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

2 Citations (Scopus)

Abstract

In this study, we describe the use of the self-organizing map (SOM) as a metamodeling technique to design a parallel text data exploration system. Firstly, the large textual collections are divided into various small data subsets. Based on the different subsets, different unitary SOM models, i.e., base models, are then trained for word clustering map. In this phase, different SOM models are implemented in parallel to gain greater computational efficiency. Finally, a SOM-based metamodel can be produced to formulate a text category map through learning from all base models. For illustration the proposed metamodel is applied to a massive text data collection. © Springer-Verlag Berlin Heidelberg 2006.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2006
Subtitle of host publication	Third International Symposium on Neural Networks, ISNN 2006, Proceedings
Publisher	Springer Verlag
Pages	1261-1266
Volume	3971 LNCS
ISBN (Print)	354034439, 9783540344391
DOIs	https://doi.org/10.1007/11759966_187
Publication status	Published - 2006
Event	3rd International Symposium on Neural Networks (ISNN 2006): Advances in Neural Networks - Chengdu, China Duration: 28 May 2006 → 1 Jun 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3971 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Symposium on Neural Networks (ISNN 2006)
Place	China
City	Chengdu
Period	28/05/06 → 1/06/06

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Lai, K. K., Yu, L., Zhou, L., & Wang, S. (2006). Self-organizing-map-based metamodeling for massive text data exploration. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings (Vol. 3971 LNCS, pp. 1261-1266). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3971 LNCS). Springer Verlag. https://doi.org/10.1007/11759966_187

Lai, Kin Keung ; Yu, Lean ; Zhou, Ligang et al. / Self-organizing-map-based metamodeling for massive text data exploration. Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. Vol. 3971 LNCS Springer Verlag, 2006. pp. 1261-1266 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Self-organizing-map-based metamodeling for massive text data exploration",

abstract = "In this study, we describe the use of the self-organizing map (SOM) as a metamodeling technique to design a parallel text data exploration system. Firstly, the large textual collections are divided into various small data subsets. Based on the different subsets, different unitary SOM models, i.e., base models, are then trained for word clustering map. In this phase, different SOM models are implemented in parallel to gain greater computational efficiency. Finally, a SOM-based metamodel can be produced to formulate a text category map through learning from all base models. For illustration the proposed metamodel is applied to a massive text data collection. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.",

author = "Lai, {Kin Keung} and Lean Yu and Ligang Zhou and Shouyang Wang",

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Lai, KK, Yu, L, Zhou, L & Wang, S 2006, Self-organizing-map-based metamodeling for massive text data exploration. in Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. vol. 3971 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3971 LNCS, Springer Verlag, pp. 1261-1266, 3rd International Symposium on Neural Networks (ISNN 2006), Chengdu, China, 28/05/06. https://doi.org/10.1007/11759966_187

Self-organizing-map-based metamodeling for massive text data exploration. / Lai, Kin Keung; Yu, Lean; Zhou, Ligang et al.
Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. Vol. 3971 LNCS Springer Verlag, 2006. p. 1261-1266 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3971 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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N2 - In this study, we describe the use of the self-organizing map (SOM) as a metamodeling technique to design a parallel text data exploration system. Firstly, the large textual collections are divided into various small data subsets. Based on the different subsets, different unitary SOM models, i.e., base models, are then trained for word clustering map. In this phase, different SOM models are implemented in parallel to gain greater computational efficiency. Finally, a SOM-based metamodel can be produced to formulate a text category map through learning from all base models. For illustration the proposed metamodel is applied to a massive text data collection. © Springer-Verlag Berlin Heidelberg 2006.

AB - In this study, we describe the use of the self-organizing map (SOM) as a metamodeling technique to design a parallel text data exploration system. Firstly, the large textual collections are divided into various small data subsets. Based on the different subsets, different unitary SOM models, i.e., base models, are then trained for word clustering map. In this phase, different SOM models are implemented in parallel to gain greater computational efficiency. Finally, a SOM-based metamodel can be produced to formulate a text category map through learning from all base models. For illustration the proposed metamodel is applied to a massive text data collection. © Springer-Verlag Berlin Heidelberg 2006.

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M3 - RGC 32 - Refereed conference paper (with host publication)

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Lai KK, Yu L, Zhou L, Wang S. Self-organizing-map-based metamodeling for massive text data exploration. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. Vol. 3971 LNCS. Springer Verlag. 2006. p. 1261-1266. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11759966_187

Self-organizing-map-based metamodeling for massive text data exploration

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