A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering.
Providing key information on how to work with research data, Introduction to Data Technologies presents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in formal education.
Dealing with the volume, complexity, and diversity of data currently being generated by scientific experiments and simulations often causes scientists to waste productive time.
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.
Exploring fundamental research questions, Conceptual Structures in Practice takes you through the basic yet nontrivial task of establishing conceptual relations as the foundation for research in knowledge representation and knowledge mining.
The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the FieldGiving 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.
Focusing on three applications of data mining, Design and Implementation of Data Mining Tools explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification.
Effective decision support systems (DSS) are quickly becoming key to businesses gaining a competitive advantage, and the effectiveness of these systems depends on the ability to construct, maintain, and extract information from data warehouses.
Interactive Graphics for Data Analysis: Principles and Examples discusses exploratory data analysis (EDA) and how interactive graphical methods can help gain insights as well as generate new questions and hypotheses from datasets.
Most widely available approaches to semantic integration provide ad-hoc, non-systematic, subjective manual mappings that lead to procrustean amalgamations to fit the target standard, an outcome that pleases no one.
Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics.
Swarm Intelligence and Its Applications in Biomedical Informatics discusses Artificial Intelligence (AI) applications in medicine and biology, as well as challenges and opportunities presented in these arenas.
Created with the input of a distinguished International Board of the foremost authorities in data mining from academia and industry, The Handbook of Data Mining presents comprehensive coverage of data mining concepts and techniques.
Data Mining and Applications in Genomics contains the data mining algorithms and their applications in genomics, with frontier case studies based on the recent and current works at the University of Hong Kong and the Oxford University Computing Laboratory, University of Oxford.
A suspenseful story about the dangers of unknowingly revealing our most intimate thoughts and actions onlineWhat happens when a naive intern is granted unfettered access to people's most private thoughts and actions?
This book investigates the potential and limitations of using Generative AI (GenAI) in terms of quality and capability in agile web development projects using React.
As software R&D investment increases, the benefits from short feedback cycles using technologies such as continuous deployment, experimentation-based development, and multidisciplinary teams require a fundamentally different strategy and process.
As software R&D investment increases, the benefits from short feedback cycles using technologies such as continuous deployment, experimentation-based development, and multidisciplinary teams require a fundamentally different strategy and process.
Artificial Intelligence (AI) innovations in digital health offer unprecedented opportunities to facilitate human health and provide tools and techniques that reduce overall costs.
Due to the complexity of operational forestry problems, computing applications are becoming pervasive in all aspects of forest and natural resource management.
Due to the complexity of operational forestry problems, computing applications are becoming pervasive in all aspects of forest and natural resource management.
Cloud computing has created a shift from the use of physical hardware and locally managed software-enabled platforms to that of virtualized cloud-hosted services.
Textual Statistics with R comprehensively covers the main multidimensional methods in textual statistics supported by a specially-written package in R.
Textual Statistics with R comprehensively covers the main multidimensional methods in textual statistics supported by a specially-written package in R.
The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique.
The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique.
Cybersecurity in Robotic Autonomous Vehicles introduces a novel intrusion detection system (IDS) specifically designed for AVs, which leverages data prioritisation in CAN IDs to enhance threat detection and mitigation.
The need for analytics skills is a source of the burgeoning growth in the number of analytics and decision science programs in higher education developed to feed the need for capable employees in this area.
The need for analytics skills is a source of the burgeoning growth in the number of analytics and decision science programs in higher education developed to feed the need for capable employees in this area.
There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms.
