Scientific Data Mining: A Practical Perspective explains how the technique of multi-disciplinary field of data mining can be used to solve modern problems of data overload in science and engineering field. Starting with a survey of problems in the application of different analysis, this book identifies common themes in this domain and use them to determine the process end-to-end scientific data mining. Multi-step process including tasks such as processing raw image or mesh data to identify objects of interest; extraction of relevant features that describe objects; detect patterns among the objects, and displays the pattern for validation by scientists. It also includes a description of system software developed for scientific data mining; general guidelines to begin the analysis of large, complex data sets, and an extensive bibliography. Audience: This book is intended for data mining practitioners and scientists interested in applying data mining techniques to data sets of them. It is also appropriate for advanced undergraduate and graduate level courses on data analysis offered in the fields of mathematics, computer science, and statistics departments. Contents: Preface, Chapter 1: Introduction, Chapter 2: Data Mining in Science and Engineering, Chapter 3: Common themes in Scientific Data Mining, Chapter 4: Scientific Data Mining Process: Chapter 5: Reducing the Size of Data, Chapter 6: Modalities fusing different data, Chapter 7: Improving Data Images; Chapter 8: Looking at Data Objects; Chapter 9: Extracting Features Describing Objects; Chapter 10: Reducing the Dimension Data, Chapter 11: Finding Patterns in Data: Chapter 12 : Data Visualization and Validating Results: Chapter 13: System for Scientific Data Mining, Chapter 14: Lessons, Challenges and Opportunities; Bibliography; Index