Unsupervised connectionist clustering algorithms for a better supervised prediction: application to a radio communication problem
Laurent Bougrain, Frédéric Alexandre · 2003
Most models concerned with real-world applications can be improved in structuring data and incorporating knowledge about the domain. In our problem of radio electrical wave dying down prediction for mobile communication, a geographic database can be divided in contextual subsets, each representing an homogeneous domain where a predictive model performs better. More precisely, by clustering the input space, a predictive model (here a multilayer perceptron) can be trained on each subspace. Various unsupervised algorithms for clustering were evaluated (Kohonen's maps, Desieno's algorithm 1988, neural gas, growing neural gas, Buhmann's algorithm 1992) to obtain classes homogeneous enough to decrease the predictive error of the radio electrical wave prediction.