Extracting Salient Dimensions for Automatic SOM Labeling  

Oleh:

Azcarraga, Arnulfo P. ; Ming-Huei Hsieh ; Pan, Shan.L. ; Setiono, Rudy

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Systems, Man, and Cybernetics: Part C Applications and Reviews vol. 35 no. 4 (Nov. 2005), page 595-600.
Topik: Market Segmentation; Self-Organizing Maps (SOM); Unsupervised SOM labelling

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II69.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Learning in self-organizing maps (SOM) is considered unsupervised because training patterns do not need accompanying desired output information. Prior to its use in some real-world applications, however, a trained SOM often has to be labeled. This labeling phase is usually supervised in that labeled patterns need accompanying output information. Because such labeled patterns are not always available or may not even be possible to construct, the supervised nature of the labeling phase restricts the deployment of SOM from a wide range of potential domains of application. This work proposes a methodical and automatic SOM labeling procedure that does not require a set of prelabeled patterns. Instead, nodes in the trained map are clustered and subsets of training patterns associated to each of the clustered nodes are identified. Salient dimensions per node cluster that constitute the bases for labeling each node in the map are then identified. The effectiveness of the method is demonstrated on a SOM-based international market segmentation study.

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