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.
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
Spectral Feature Selection for Data Mining
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 ...
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 ...
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 ...
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 ...
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...
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 ...
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 ...
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 ...
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, ...
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 ...
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 ...