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An incremental parallel neural network for unsupervised classification
This paper presents a novel unsupervised and parallel learning technique for data clustering that are polluted by noise using neural network approaches. The proposed approach is based on a self-organizing incremental neural network. The design of two-layer neural network enables this system to repre...
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Main Authors: | , , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
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Summary: | This paper presents a novel unsupervised and parallel learning technique for data clustering that are polluted by noise using neural network approaches. The proposed approach is based on a self-organizing incremental neural network. The design of two-layer neural network enables this system to represent the topological structure of unsupervised on-line data, reports the reasonable number of clusters, and gives typical prototype patterns of every cluster without prior conditions such as a suitable number of nodes. To confirm the efficiency of the proposed learning mechanism, we present a set of experiments with artificial data sets and real world data sets. |
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DOI: | 10.1109/WOSSPA.2011.5931521 |