Abstract
Clustering process is defined as grouping similar objects together into homogeneous groups or clusters. Objects that belong to one cluster should be very similar to each other, but objects in different clusters will be dissimilar. It aims to simplify the representation of the initial data. The automatic classification recovers all the methods allowing the automatic construction of such groups. This paper describes the design of radial basis function (RBF) neural classifiers using a new algorithm for characterizing the hidden layer structure. This algorithm, called k-means Mahalanobis distance, groups the training data class by class in order to calculate the optimal number of clusters of the hidden layer, using two validity indexes. To initialize the initial clusters of k-means algorithm, the method of logarithmic spiral golden angle has been used. Two real data sets (Iris and Wine) are considered to improve the efficiency of the proposed approach and the obtained results are compared with basic literature classifier
Highlights
Clustering is one of the most useful tasks in data mining process for discovering groups and identifying interesting distributions and patterns in the underlying data
radial basis function (RBF) neural networks consist of three layers: an input layer, a hidden layer and an output layer
The input layer corresponds to the input vector feature space and the output layer corresponds to the pattern classes [2]
Summary
Clustering is one of the most useful tasks in data mining process for discovering groups and identifying interesting distributions and patterns in the underlying data. A new learning algorithm is proposed for the construction of the radial basis function networks solving classification problems. It determines the proper number of hidden neurons automatically and calculates the centers values of radial basis functions. After the selection of the hidden neurons, the widths of nodes are determined by the P-nearest neighbors heuristic, and the weights between the hidden layer and the output layer are calculated by the pseudo-inverse matrix. The aim of this approach consists in transforming the problem of determining the number of hidden layer neurons to a clustering problem.
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