A lot of scientific research has been focused to explore methods to analyze information in huge sets of data. Different types of algorithms and statistical techniques have been developed to reveal relationships that may exist in large multi-dimensional data sets. Once the data has been organized, it can be used in subsequent hypothesis or decision making. Organizing huge sets of data may only be done automatically by using an algorithmic approach. This is known as data clustering or cluster analysis. This book is about how to use machine learning methods to cluster the data. Such algorithms may be used in business intelligence, market research, pattern recognition, image analysis and biomedical research. In this book we show how we can use such algorithms combined with visualization techniques to cluster proteomics data. One often has to do analysis without a priori information available about the underlying nature of the data. Data clustering can in such cases be useful to uncover hidden relationships that may exist in the data before doing further analysis.