Q12,paper 3, june 12 .Which of the following can be used for clustering of data ?

Options (A) Single layer perception (B) Multilayer perception (C) Self organizing map (D) Radial basis function
Answer© Self organizing map. Explanation. Classification is supervised learning technique used to assign per-defined tag to instance on the basis of features. So classification algorithm requires training data. Classification model is created from training data, then classification model is used to classify new instances. Clustering is unsupervised technique used to group similar instances on the basis of features. Clustering does not require training data. Clustering does not assign per-defined label to each and every group. In machine learning, the perceptron is an algorithm for supervised classification of an input into one of several possible non-binary outputs. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. The algorithm allows for online learning, in that it processes elements in the training set one at a time. A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map. Sums of radial basis functions are typically used to approximate given functions