As data mining algorithms are typically applied to sizable volumes of high-dimensional data, these can result in large storage requirements and inefficient computation times.

This unique text/reference addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain. Schemes are presented which are shown to be efficient both in terms of space and time, while simultaneously providing the same or better classification accuracy, as illustrated using high-dimensional handwritten digit data and a large intrusion detection dataset.

Topics and features: presents a concise introduction to data mining paradigms, data compression, and mining compressed data; describes a non-lossy compression scheme based on run-length encoding of patterns with binary valued features; proposes a lossy compression scheme that recognizes a pattern as a sequence of features and identifying subsequences; examines whether the identification of prototypes and features can be achieved simultaneously through lossy compression and efficient clustering; discusses ways to make use of domain knowledge in generating abstraction; reviews optimal prototype selection using genetic algorithms; suggests possible ways of dealing with big data problems using multiagent systems.

A must-read for all researchers involved in data mining and big data, the book proposes each algorithm within a discussion of the wider context, implementation details and experimental results. These are further supported by bibliographic notes and a glossary."""

Although industry has been leveraging the advancements of component-oriented development and assembly (CODA) technology for some time, there has long been a need for a book that provides a complete overview of the multiple technologies that support CODA. Filling this need, Component-Oriented Development and Assembly supplies comprehensive coverage of the principles, practice, and paradigm of component-oriented development and assembly. The first part of the book provides the conceptual foundation for component-oriented software. Part II focuses on the various standard Java component models and describes how to develop a component-oriented system using these component models. Part III covers the various aspects of the component-oriented development paradigm. Based on the authors' research and teaching experience, the text focuses on the principles of component-oriented software development from a technical concepts perspective, designer's perspective, programmer's perspective, and manager's perspective. Covering popular component development frameworks based on Java, it is suitable as a textbook for component-oriented software for undergraduate and postgraduate courses. It is also an ideal reference for anyone looking to adopt the component-oriented development paradigm. The book provides readers with access to all the source code used in the book on a companion site (http://www.codabook.com). The source code for the CODA implementation of the case study presented in Chapter 11 is also hosted on the website. The website will also serve as a technical forum for further discussions on the topic and for any updates to the book.

This book addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain. Schemes are presented which are shown to be efficient both in terms of space and time, while simultaneously providing the same or better classification accuracy. Features: describes a non-lossy compression scheme based on run-length encoding of patterns with binary valued features; proposes a lossy compression scheme that recognizes a pattern as a sequence of features and identifying subsequences; examines whether the identification of prototypes and features can be achieved simultaneously through lossy compression and efficient clustering; discusses ways to make use of domain knowledge in generating abstraction; reviews optimal prototype selection using genetic algorithms; suggests possible ways of dealing with big data problems using multiagent systems.