Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
9,454
result(s) for
"COMPUTERS Databases Data Mining."
Sort by:
Data mining : concepts and techniques
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.
eBook
Data Clustering
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, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process-including how to verify the quality of the underlying clusters-through supervision, human intervention, or the automated generation of alternative clusters.
eBook
Veracity of big data : machine learning and other approaches to verifying truthfulness
Examine the problem of maintaining the quality of big data and discover novel solutions. You will learn the four V's of big data, including veracity, and study the problem from various angles. The solutions discussed are drawn from diverse areas of engineering and math, including machine learning, statistics, formal methods, and the Blockchain technology. Veracity of Big Data serves as an introduction to machine learning algorithms and diverse techniques such as the Kalman filter, SPRT, CUSUM, fuzzy logic, and Blockchain, showing how they can be used to solve problems in the veracity domain. Using examples, the math behind the techniques is explained in easy-to-understand language. Determining the truth of big data in real-world applications involves using various tools to analyze the available information. This book delves into some of the techniques that can be used. Microblogging websites such as Twitter have played a major role in public life, including during presidential elections. The book uses examples of microblogs posted on a particular topic to demonstrate how veracity can be examined and established. Some of the techniques are described in the context of detecting veiled attacks on microblogging websites to influence public opinion. -- Back cover.
Book
Methodological developments in data linkage
A comprehensive compilation of new developments in data linkage methodology
The increasing availability of large administrative databases has led to a dramatic rise in the use of data linkage, yet the standard texts on linkage are still those which describe the seminal work from the 1950-60s, with some updates. Linkage and analysis of data across sources remains problematic due to lack of discriminatory and accurate identifiers, missing data and regulatory issues. Recent developments in data linkage methodology have concentrated on bias and analysis of linked data, novel approaches to organising relationships between databases and privacy-preserving linkage.
Methodological Developments in Data Linkage brings together a collection of contributions from members of the international data linkage community, covering cutting edge methodology in this field. It presents opportunities and challenges provided by linkage of large and often complex datasets, including analysis problems, legal and security aspects, models for data access and the development of novel research areas. New methods for handling uncertainty in analysis of linked data, solutions for anonymised linkage and alternative models for data collection are also discussed.
Key Features:
* Presents cutting edge methods for a topic of increasing importance to a wide range of research areas, with applications to data linkage systems internationally
* Covers the essential issues associated with data linkage today
* Includes examples based on real data linkage systems, highlighting the opportunities, successes and challenges that the increasing availability of linkage data provides
* Novel approach incorporates technical aspects of both linkage, management and analysis of linked data
This book will be of core interest to academics, government employees, data holders, data managers, analysts and statisticians who use administrative data. It will also appeal to researchers in a variety of areas, including epidemiology, biostatistics, social statistics, informatics, policy and public health.
eBook
Discovering knowledge in data : an introduction to data mining
The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before.
This book provides the tools needed to thrive in today's big data world. The author demonstrates how to leverage a company's existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will \"learn data mining by doing data mining\". By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining.
* The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis.
* Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization
* Offers extensive coverage of the R statistical programming language
* Contains 280 end-of-chapter exercises
* Includes a companion website for university instructorswho adopt the book
eBook
Machine Learning for Healthcare
Machine Learning for Healthcare: Handling and Managing
Data
will provide in-depth information about handling and
managing healthcare data through machine learning methods. This book will express
the long-standing challenges in healthcare informatics and provide rational
explanations of how to deal with them.
Machine Learning for Healthcare: Handling and Managing
Data
provides techniques on how to apply machine learning within
your organization and evaluate the efficacy, suitability, and efficiency of machine
learning applications. These are illustrated in a case study which examines how
chronic disease is being redefined through patient-led data learning and the
Internet of Things. This text offers a guided tour of machine learning algorithms,
architecture design, and applications of learning in healthcare. Readers will
discover the ethical implications of machine learning in healthcare and the future
of machine learning in population and patient health optimization. This book can
also help assist in the creation a machine learning model, performance evaluation,
and the operationalization of its outcomes within organizations. This book may
appeal to Computer Science/Information Technology professionals and researchers
working in the area of machine learning, and is especially applicable to the
healthcare sector.
The features of this book include:
A unique and complete focus on applications of machine learning in the
healthcare sector.
An examination of how data analysis can be done using healthcare data and
bioinformatics.
An investigation of how healthcare companies can leverage the tapestry of
big data to discover new business values.
An exploration of the concepts of machine learning, along with recent
research developments in healthcare sectors.
eBook
Linked Data Management
This book presents techniques for querying and managing Linked Data that is available on today's Web. It shows how the abundance of Linked Data can serve as fertile ground for research and commercial applications. While the book covers query processing extensively, the Linked Data abstraction furnishes more than a mechanism for collecting, integrating, and querying data from the open Web-the Linked Data technology stack also allows for controlled, sophisticated applications deployed in an enterprise environment.
eBook
Data mining and learning analytics : applications in educational research
Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates.
eBook