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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.

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Statistical Data Mining Using SAS Applications

Statistical Data Mining Using SAS Applications

2nd Edition

George Fernandez
June 18, 2010

Statistical Data Mining Using SAS Applications, Second Edition describes statistical data mining concepts and demonstrates the features of user-friendly data mining SAS tools. Integrating the statistical and graphical analysis tools available in SAS systems, the book provides complete statistical ...

Knowledge Discovery from Data Streams

Knowledge Discovery from Data Streams

1st Edition

Joao Gama
May 25, 2010

Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing ...

Information Discovery on Electronic Health Records

Information Discovery on Electronic Health Records

1st Edition

Vagelis Hristidis
December 10, 2009

Exploiting the rich information found in electronic health records (EHRs) can facilitate better medical research and improve the quality of medical practice. Until now, a trivial amount of research has been published on the challenges of leveraging this information. Addressing these challenges, ...

Text Mining Classification, Clustering, and Applications

Text Mining: Classification, Clustering, and Applications

1st Edition

Ashok N. Srivastava, Mehran Sahami
June 15, 2009

The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the Field Giving a broad perspective of the field from numerous vantage points, Text Mining: Classification, Clustering, and Applications focuses on statistical methods for text mining and analysis. It ...

Geographic Data Mining and Knowledge Discovery

Geographic Data Mining and Knowledge Discovery

2nd Edition

Harvey J. Miller, Jiawei Han
May 27, 2009

The Definitive Volume on Cutting-Edge Exploratory Analysis of Massive Spatial and Spatiotemporal Databases Since the publication of the first edition of Geographic Data Mining and Knowledge Discovery, new techniques for geographic data warehousing (GDW), spatial data mining, and geovisualization (...

The Top Ten Algorithms in Data Mining

The Top Ten Algorithms in Data Mining

1st Edition


April 09, 2009

Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each ...

Constrained Clustering Advances in Algorithms, Theory, and Applications

Constrained Clustering: Advances in Algorithms, Theory, and Applications

1st Edition

Sugato Basu, Ian Davidson, Kiri Wagstaff
August 18, 2008

Since the initial work on constrained clustering, there have been numerous advances in methods, applications, and our understanding of the theoretical properties of constraints and constrained clustering algorithms. Bringing these developments together, Constrained Clustering: Advances in ...

Computational Methods of Feature Selection

Computational Methods of Feature Selection

1st Edition

Huan Liu, Hiroshi Motoda
October 29, 2007

Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery. Highlighting current research issues, Computational...

Automated Data Analysis Using Excel

Automated Data Analysis Using Excel

1st Edition

Brian D. Bissett
June 15, 2007

Because the analysis of copious amounts of data and the preparation of custom reports often take away time from true research, the automation of these processes is paramount to ensure productivity. Exploring the core areas of automation, report generation, data acquisition, and data analysis, ...

Understanding Complex Datasets Data Mining with Matrix Decompositions

Understanding Complex Datasets: Data Mining with Matrix Decompositions

1st Edition

David Skillicorn
May 17, 2007

Making obscure knowledge about matrix decompositions widely available, Understanding Complex Datasets: Data Mining with Matrix Decompositions discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a broad range of application areas. Without ...

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