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.
Social Computing A Data Mining Perspective
Spatial and Spatiotemporal Data Mining
Automated Data Analysis Using Excel
Advanced Data Science and Analytics with Python
Introduction to Computational Health Informatics
Industrial Applications of Machine Learning
Exploratory Data Analysis Using R
Data Clustering Algorithms and Applications
Edited By R. Bharat Rao, Glenn Fung, Romer Rosales
June 01, 2021
Focusing on several recent, novel machine learning automatic algorithms, this reference provides the first source on the use of machine learning methods in designing computer-aided diagnosis (CAD) systems. It proposes a framework for CAD problems, presents the technical issues involved when ...
By Huan Liu, Jianping Zhang, Arunabha Sen
June 01, 2021
This book begins with a brief introduction to social computing and a review of classic graph theory and game theory. It then examines the data used to construct social networks, focusing on emerging Web 2.0 technologies and social networking websites, such as Facebook and MySpace. The book also ...
Edited By SHASHI SHEKHAR, Ranga Raju Vatsavai
June 01, 2021
A collaborative effort between a leading academician who has laid foundation to the spatial data mining and a research scientist from a prestigious national laboratory who has over a decade of interdisciplinary research experience, this book covers not only concepts and algorithms, but also ...
By Brian D. Bissett
August 19, 2020
This new edition covers some of the key topics relating to the latest version of MS Office through Excel 2019, including the creation of custom ribbons by injecting XML code into Excel Workbooks and how to link Excel VBA macros to customize ribbon objects. It now also provides examples in using ...
By Jesus Rogel-Salazar
May 05, 2020
Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with ...
By Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
January 14, 2020
This class-tested textbook is designed for a semester-long graduate or senior undergraduate course on Computational Health Informatics. The focus of the book is on computational techniques that are widely used in health data analysis and health informatics and it integrates computer science and ...
By Zhongfei Zhang, Ruofei Zhang
October 18, 2019
Collecting the latest developments in the field, Multimedia Data Mining: A Systematic Introduction to Concepts and Theory defines multimedia data mining, its theory, and its applications. Two of the most active researchers in multimedia data mining explore how this young area has rapidly developed ...
By David Skillicorn
September 19, 2019
Most of the research aimed at counterterrorism, fraud detection, or other forensic applications assumes that this is a specialized application domain for mainstream knowledge discovery. Unfortunately, knowledge discovery changes completely when the datasets being used have been manipulated in order...
Edited By Francesco Bonchi, Elena Ferrari
September 05, 2019
Covering research at the frontier of this field, Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, that face the increasing heterogeneity and ...
By Pedro Larrañaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, Carlos Esteban Puerto-Santana, Concha Bielza
December 10, 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 ...
By 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 ...
Edited By Charu C. Aggarwal, Chandan K. Reddy
August 21, 2013
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, ...