Learning speed of supervised neural networks as similarity measurement in unsupervised cluster analysis

Article type: Research Article

Authors: Khashei, Mehdi

Affiliations: Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran | Tel.: +98 311 3912550 1; Fax: +98 311 3915526; E-mail: Khashei@cc.iut.ac.ir

Abstract: Cluster analysis or clustering is one of the most important and widely used techniques for data exploration and knowledge discovery that concerned with partitioning a set of objects in such a way that objects in the same groups, called clusters, are more similar to each other than to those in other clusters. However, obtaining the clusters that exhibit high within-cluster similarity or homogeneity and high between-cluster dissimilarity or heterogeneity is critically depended on the similarity notion, which has not been yet clearly defined for clustering purposes. Distance and correlation are the most important and commonly used mathematics and statistics-based similarity measurements in the literature of the clustering, respectively. In this paper, the learning speed of the supervised neural networks is proposed as novel intelligent similarity measurement for unsupervised clustering problems. On the other hand, the main aim of this paper is to answer this question that can convergence speed of the different objects to the given target be used for measuring the similarity. Empirical results of the simulated data sets indicate that the proposed measurement not only can be used as similarity measurement in clustering tasks, but also can produce accurate results. In this way, for first time and in contrast of the literature, it is demonstrated that a supervised model can be used for handling the unsupervised tasks.

Keywords: Classification & clustering, similarity measurement, Artificial Neural Networks (ANNs), supervised and unsupervised processes, pattern recognition, data mining

DOI: 10.3233/IDA-160165

Journal: Intelligent Data Analysis, vol. 21, no. 4, pp. 867-892, 2017