This book is divided into two parts. The first part is about non-redundant clustering and feature selection for high dimensional data. The second part is on applying learning techniques to lung tumor image-guided radiotherapy. In the first part, a new clustering paradigm is investigated for exploratory data analysis: find all non-redundant clustering views of the data. Also a feature selection method is developed based on the popular transformation approach: principal component analysis (PCA). In the second part, machine learning algorithms are designed to aid lung tumor image-guided radiotherapy (IGRT). Specifically, intensive studies are preformed for gating and for directly tracking the tumor. For gating, two methods are developed: (1) an ensemble of templates where the representative templates are selected by Gaussian mixture clustering, and (2) a support vector machine (SVM) classifier with radial basis kernels. For the tracking problem, a multiple- template matching method is explored to capture the varying tumor appearance throughout the different phases of the breathing cycle.