The size of real-world datasets is growing at an extraordinary rate because of the advance of instrumentation, ever-increasing computing power, and the availability of extremely large and cheap storage devices. As these data become more detailed and multi-dimensional (space and time), it becomes ever more difficult for analysts to sift through the data even though it may contain valuable information. Data mining holds great promise to address this challenge by providing efficient techniques to uncover useful information hidden in the large data repositories. The key objective of this tutorial is to introduce the major principles and techniques used in data mining from an algorithmic perspective. A study of these principles and techniques is essential for developing a better understanding of how data mining techniques can be applied to various types of data. The core topics to be covered in this tutorial include four data mining tasks: classification, clustering, association analysis, and anomaly/novelty detection. We will present some algorithms and case studies to illustrate these data mining tasks.