A Novel Soil Profile Feature Reduction Model using Principal Component Analysis

Background/Objectives: Data Mining has been used to analyze large datasets and establish useful classification and patterns in the datasets. The efficient analysis of data in different format becomes a challenging work. Methods/Statistical Analysis: This work proposed a novel Soil Profile Feature Reduction Model using Principal Component Analysis for data reduction. The proposed model uses the method of k-Means clustering and PC Aapproach for feature reduction which initially applies PCA to acquire reduced uncorrelated attributes showing maximal eigenvalues in the dataset with minimum loss of information. Again proposed model uses k-Means on the PCA reduced dataset to find out discriminative features that will be the most sufficient ones for classification. Findings: The weight by PCA generates attribute weights of the soil profile dataset using a component created by the PCA. The component is mentioned by the component number parameter. The normalize weight parameter is usually set to true to spread the weights between 0 and 1. The attribute weights reflect the relevance of the attributes with respect to the class attribute. The higher weight of an attribute is more relevant, it is considered. This is a combination of clustering approach with feature reduction to get a minimal set attributes relating a suitably high accuracy in describing the original features. The result of clustering is same after reducing the attributes using PCA. The experimental results prove that proposed model is reducing number of initial attributes, reducing computational complexity and improving predictive accuracy in High Dimensional Datasets. Applications/Improvements: The same soil profile feature is implemented by using the other techniques instead of PCA algorithm in future.