Feature reduction using principal component analysis for agricultural data set

Many applications like video surveillance, telecommunication, weather forecasting and sensor networks uses high volume of data of different types. The effective and efficient analysis of data in such different forms becomes a challenging task. Analysis of such large expression data gives rise to a number of new computational challenges not only due to the increase in number of data objects but also due to the increase in number of attributes. Hence, to improve the efficiency and accuracy of mining task on high dimensional data, the data must be preprocessed by an efficient dimensionality reduction method. In this paper, we have proposed to use the method of k-means clustering and principal component analysis (PCA) approach for attribute reduction, which initially apply PCA to obtain reduced uncorrelated attributes specifying maximal eigenvalues in the dataset with minimum loss of information. Then again we proposed to use k-means on the PCA reduced dataset to discover discriminative features that will be the most adequate ones for classification. This is a combination of clustering approach with feature reduction to obtain a minimal set attributes retaining a suitably high accuracy in representing the original features. We have used the Greengram agricultural data set. Finally, we found that the result of clustering is same after reducing the attributes using PCA.