A novel approach for distance-based semi-supervised clustering using functional link neural network

Semi-supervised clustering is gaining importance these days since neither supervised nor unsupervised learning methods in a stand-alone manner provide satisfactory results. Existing semi-supervised clustering techniques are mostly based on pair-wise constraints, which could be misleading. These semi-supervised clustering algorithms also fail to address the problem of dealing with attributes having different weights. In most of the real-life applications, all attributes do not have equal importance and hence same weights cannot be assigned for each attribute. In this paper, a novel distance-based semi-supervised clustering algorithm has been proposed, which uses functional link neural network (FLNN) for finding weights for attributes with small amount of labeled data for further use in parametric Minkowski's model for clustering. In FLNN, the nonlinearity is captured by enhancing the input using orthonormal basis functions. The effectiveness of the approach has been illustrated over a number of datasets taken from UCI machine learning repository. Comparative performance evaluation demonstrates that the proposed approach outperforms the existing semi-supervised clustering algorithms. The proposed approach has also been successfully used to cluster the crime locations and to find crime hot spots in India on the data provided by National Crime Records Bureau (NCRB).