Abstract
In this paper, we present a simple and fast way to provide the operator a plane representation of multidimensional data through neural networks for interactive classification. The superiority of humans over automatic clustering procedures comes from their ability in recognising cluster structures in a two dimensional space, even in the presence of outliers between the clusters, of bridging clusters and of all kinds of irrelevant details in the data points distribution. When giving the operator the interactive means which will help him to isolate clusters of two dimensional points, this visualisation becomes base of a clustering procedure where the operator doesn’t loose his grip on the data he is analysing.
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Daoudi, M., Hamad, D., Postaire, JG. (1993). Interactive Classification through Neural Networks. In: Albrecht, R.F., Reeves, C.R., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7533-0_13
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DOI: https://doi.org/10.1007/978-3-7091-7533-0_13
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82459-7
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