Clustering variables by classical approaches and neural network Boolean factor analysis
In this paper, we compare three methods for grouping of binary variables: neural network Boolean factor analysis, hierarchical clustering, and a linear factor analysis on the mushroom dataset. In contrast to the latter two traditional methods, the advantage of neural network Boolean factor analysis...
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| creator | Frolov, A. Husek, D. Rezankova, H. Snasel, V. Polyakov, P. |
| description | In this paper, we compare three methods for grouping of binary variables: neural network Boolean factor analysis, hierarchical clustering, and a linear factor analysis on the mushroom dataset. In contrast to the latter two traditional methods, the advantage of neural network Boolean factor analysis is its ability to reveal overlapping classes in the dataset. It is shown that the mushroom dataset provides a good demonstration of this advantage because it contains both disjunctive and overlapping classes. |
| doi_str_mv | 10.1109/IJCNN.2008.4634335 |
| format | Conference Proceeding |
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| ispartof | 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008, Vol.10, p.3742-3746 |
| issn | 2161-4393 1522-4899 2161-4407 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Artificial neural networks Computer science Electronic mail Joints Loading Reactive power |
| title | Clustering variables by classical approaches and neural network Boolean factor analysis |
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