As the field of data mining and knowledge discovery continues to grow, the timely dissemination of emerging research has become increasingly important both in math and stats, as well as across a range of disciplines seeking to take advantage of the wealth of data made available through informatics. This series aims to capture new developments and applications in data mining and knowledge discovery, while summarizing the computational tools and techniques useful in data analysis. This series is being established to encourage the integration of mathematical, statistical, and computational methods and techniques through the publication of a broad range of textbooks, reference works, and handbooks. We are looking to include those single author and contributed works that will—
The inclusion of concrete examples and applications is highly encouraged. The scope of the series includes, but is not limited to, titles in the areas of data mining and knowledge discovery methods and applications, modeling, algorithms, theory and foundations, data and knowledge visualization, data mining systems and tools, and privacy and security issues. We are willing to consider other relevant topics that might be proposed by potential contributors.
RapidMiner, Second Edition: Data Mining Use Cases and Business Analytics Applications
Computational Health Informatics
Exploratory Data Analysis Using R
Spectral Feature Selection for Data Mining
Data Science and Analytics with Python
Social Networks with Rich Edge Semantics
Data Mining for Design and Marketing
Markus Hofmann, Ralf Klinkenberg
May 31, 2019
Written by leaders in the data mining community, including the developers of the RapidMiner software, this book provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other...
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
May 15, 2019
This class-tested textbook is designed for a semester-long graduate, or senior undergraduate course on Computational Health Informatics. Integrating a computer science perspective with a clinical perspective, the book is designed to prepare computer science students for careers in computational...
Pedro Larrañaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, Carlos Esteban Puerto-Santana, Concha Bielza
December 18, 2018
Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces...
Robert C. Hughes
August 22, 2018
Human Capital Systems, Analytics, and Data Mining provides human capital professionals, researchers, and students with a comprehensive and portable guide to human capital systems, analytics and data mining. The main purpose of this book is to provide a rich tool set of methods and tutorials for...
Ronald K. Pearson
May 29, 2018
Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to...
Zheng Alan Zhao, Huan Liu
April 18, 2018
Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified...
Guozhu Dong, Huan Liu
April 04, 2018
Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the...
August 16, 2017
Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and...
Ashok N. Srivastava, Ramakrishna Nemani, Karsten Steinhaeuser
August 07, 2017
From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an...
Quan Zheng, David Skillicorn
August 01, 2017
Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and...
David Lo, Siau-Cheng Khoo, Jiawei Han, Chao Liu
June 14, 2017
An emerging topic in software engineering and data mining, specification mining tackles software maintenance and reliability issues that cost economies billions of dollars each year. The first unified reference on the subject, Mining Software Specifications: Methodologies and Applications describes...
Yukio Ohsawa, Katsutoshi Yada
June 07, 2017
Data Mining for Design and Marketing shows how to design and integrate data mining tools into human thinking processes in order to make better business decisions, especially in designing and marketing products and systems. The expert contributors discuss how data mining can identify valuable...