This comprehensive professional reference for scientists, engineers, and researchers brings together in a single resource all the information a beginner will need to rapidly learn how to conduct data mining and the statistical analysis required to interpret the data once mined. A glossary of data mining terms provided in the appendix.
One of the most popular stemming algorithms, the Porter stemmer, used a series of heuristic replacement rules to transform word tokens into their stemmed form (Porter, 1980).1 These rules have been refined in later versions into what is ...
Created with the input of a distinguished International Board of the foremost authorities in data mining from academia and industry, The Handbook of Data Mining presents comprehensive coverage of data mining concepts and techniques.
The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems
This book presents some of the most important modeling and prediction techniques, along with relevant applications.
This is the sixth version of this successful text, and the first using Python.
This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
This - one of a kind - book offers a comprehensive, almost encyclopedic presentation of statistical methods and analytic approaches used in science, industry, business, and data mining, written from the perspective of the real-life ...
This book focuses on the importance of clean, well-structured data as the first step to successful data mining.
This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data.
You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python.