Factor Knowledge Mining Using the Techniques of AI Neural Networks and Self-Organizing Map
This paper offers a hybrid technique combined by artificial neural networks (ANN) and self-organizing map (SOM) as a way to explore factor knowledge. ANN and SOM are two kinds of pattern classification techniques based on supervised and unsupervised mechanisms, respectively. This paper proposes a ne...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Hindawi - SAGE Publishing
2015-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/412418 |
| Summary: | This paper offers a hybrid technique combined by artificial neural networks (ANN) and self-organizing map (SOM) as a way to explore factor knowledge. ANN and SOM are two kinds of pattern classification techniques based on supervised and unsupervised mechanisms, respectively. This paper proposes a new aspect to combine ANN and SOM as NNSOM process in order to delve into factor knowledge other than pattern classification. The experimental material is conducted by the investigation of street night market in Taiwan. NNSOM process can yield two results about factor knowledge: first, which factor is the most important factor for the development of street night market; second, what value of this factor is most positive to the development of street night market. NNSOM process can combine the advantages of supervised and unsupervised mechanisms and be applied to different disciplines. |
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| ISSN: | 1550-1477 |