ISSN : 1796-2021
Volume : 2    Issue : 6    Date : November 2007

Generalization Capabilities Enhancement of a Learning System by Fuzzy Space Clustering
Zakaria Nouir, Berna Sayrac, Benoît Fourestié, Walid Tabbara, and Françoise Brouaye
Page(s): 30-37
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We have used measurements taken on real network to enhance the performance of our radio
network planning tool. A distribution learning technique is adopted to realize this challenged task.
To ensure better generalization capabilities of the learning algorithm, a preprocessing of data is
required and involves the use of a clustering algorithm that divides the whole learning space into
subspaces. In this paper we apply a new fuzzy clustering algorithm to a prediction tool of a third
generation (3G) cellular radio network. Results show that the differences observed between
simulations and measurements can be considerably diminished and the generalization capacity is
enhanced thanks to the proposed clustering algorithm. This algorithm performs well than classical
k-means algorithm.We can then predict with enhanced accuracy new configuration for which we don’
t have measurements, as long as they are not very different from learned configurations.

Index Terms
Radio Network Prediction, Measurements, Distribution Learning, k-means, Fuzzy Clustering