Uni-class pattern-based classification model

This paper presents a model of a supervised machine learning approach for classification of a dataset. The model extracts a set of patterns common in a single class from the training dataset according to the rules of the pattern-based subspace clustering technique. These extracted patterns are used to classify the objects of that class in the testing dataset. The user-defined threshold dependence problem in this clustering technique has been resolved in the proposed model. Also this model solve the curse of dimensionality problem without the need of using a separate dimensionality reduction method. Another distinguishing point in this model is its dependence on the variation of the values of relative features among different objects. Experimental results on synthetic and real life datasets show that this approach is more efficient and effective than the existing techniques.