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Chapman & Hall/CRC Data Mining and Knowledge Discovery Series


About the Series

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—

  • Provide introductory and advanced instructional and reference material for students and professionals in the mathematical, statistical, and computational sciences
  • Supply researchers with the latest discoveries and the resources they need to advance the field
  • Offer assistance to those interdisciplinary researchers and practitioners seeking to make use of data mining technology without advanced mathematical backgrounds

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.

49 Series Titles

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Exploratory Data Analysis Using R

Exploratory Data Analysis Using R

Forthcoming

Ronald K. Pearson
May 15, 2018

This textbook introduces exploratory data analysis (EDA) and covers the range of interesting features we can expect to find in data. The book also explores the practical mechanics of using R to do EDA....

Feature Engineering for Machine Learning and Data Analytics

Feature Engineering for Machine Learning and Data Analytics

Forthcoming

Guozhu Dong, Huan Liu
March 19, 2018

Edited by two of the leading experts in the field, this book provides a comprehensive reference book on feature engineering. The book will provide a description of problems/applications/dataset types suitable for feature engineering, as well as techniques, principles, issues and challenges for...

Spectral Feature Selection for Data Mining

Spectral Feature Selection for Data Mining

Forthcoming

Zheng Alan Zhao, Huan Liu
November 30, 2017

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...

Social Networks with Rich Edge Semantics

Social Networks with Rich Edge Semantics

Quan Zheng, David Skillicorn
August 18, 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...

Data Science and Analytics with Python

Data Science and Analytics with Python

Jesus Rogel-Salazar
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...

Large-Scale Machine Learning in the Earth Sciences

Large-Scale Machine Learning in the Earth Sciences

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...

Mining Software Specifications: Methodologies and Applications

Mining Software Specifications: Methodologies and Applications

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...

Data Mining for Design and Marketing

Data Mining for Design and Marketing

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...

Biological Data Mining

Biological Data Mining

Jake Y. Chen, Stefano Lonardi
June 07, 2017

Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter...

Data Mining with R: Learning with Case Studies, Second Edition

Data Mining with R: Learning with Case Studies, Second Edition

Luis Torgo
January 19, 2017

Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material,...

Data Mining: A Tutorial-Based Primer, Second Edition

Data Mining: A Tutorial-Based Primer, Second Edition

Richard J. Roiger
December 01, 2016

Data Mining: A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and...

Advances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy

Michael J. Way, Jeffrey D. Scargle, Kamal M. Ali, Ashok N. Srivastava
November 16, 2016

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount...

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