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Self-Organizing Maps for Mixed Feature-Type Symbolic Data

Abstract : The self-organizing map is a kind of artificial neural network used to map high dimensional data into a low dimensional space. This paper presents a self-organizing map to do unsupervised clustering for mixed feature-type symbolic data while preserving the topology of the data. A preprocessing technique prior to clustering is needed in order to homogenize the data. Every mixed feature-type vector is transformed into a vector of histograms. The resulting data set is used to train the self-organizing map using the batch algorithm. Similar input vectors will be allocated to the same neuron or to a neighbor neuron on the map. The performance of this approach is then illustrated and discussed while applied to real interval and mixed feature-type symbolic data sets.
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Contributor : Alexandra Siebert <>
Submitted on : Tuesday, January 15, 2013 - 10:12:50 AM
Last modification on : Monday, December 14, 2020 - 12:38:05 PM




Chantal Hajjar, Hani Hamdan. Self-Organizing Maps for Mixed Feature-Type Symbolic Data. 2012 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2012), Dec 2012, Ho Chi Minh City, Vietnam. ⟨10.1109/ISSPIT.2012.6621275⟩. ⟨hal-00776147⟩



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