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In this paper, we propose adaptive K-means algorithm upon the principal component analysis PCA feature extraction to pattern recognition by using a neural network model. Adaptive k-means to discriminate among objects belonging to different groups based upon the principal component analysis PCA implemented for statistical feature extraction. The features extracted by PCA consistently reduction dimensional algorithm, thus demonstrating that the suite of structure detectors effectively performs generalized feature extraction. The classification accuracies achieved using feature learning process of back propagation neural network . A comparison of the proposed adaptive and previous non-adaptive ensemble is the primary goal of the experiments. We evaluated the performance of the clustering ensemble algorithms by matching the detected and the known partitions of the iris dataset. The best possible matching of clusters provides a measure of performance expressed as the misassignment rate.

Neural Network and Adaptive Feature Extraction Technique for Pattern Recognition