• A LINEAR TRANSFORMATION FOR DIMENSIONALITY REDUCTION IN HIGH DIMENSIONAL DATASETS USING PRINCIPLE COMPONENT ANALYSIS

Abstract

Data mining is the use of automated data analysis techniques to uncover previously undetected relationships among data items. Due to the mega high dimensionality nature of datasets, data dimension reduction has drawn special attention for such type of data analysis. Data Reduction can be viewed as preprocessing step which removes distracting variance from the datasets so that clustering, classifiers can estimators perform better. In this paper principal component analysis, a linear transformation is used for dimensionality reduction and clustering with K-Medoids algorithm is applied and shows the results.