ABSTRACT
Chapter 11 deals with clustering techniques and principal component analysis (PCA). K-means clustering, one of the most widely used clustering techniques, is explained clearly with the aid of a sample dataset. A video link of the same by the authors is provided for better understanding. K-means clustering is implemented on a sample dataset aided by stage-by-stage screenshots. The chapter proceeds to address one of the most important concepts in machine learning, PCA, with in-depth understanding. The algorithmic steps are listed, followed by a demonstration to show the implementation of PCA with the powerful oneAPI programming model by considering an appropriate sample dataset to determine its performance improvements. The complete codes and datasets are made available in the GitHub links provided. The chapter is supplemented by additional learning resources, a quiz and key points to be remembered.
