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.

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."""

This book constitutes the refereed proceedings of the 5th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2013, held in Kolkata, India in December 2013. The 101 revised papers presented together with 9 invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on pattern recognition; machine learning; image processing; speech and video processing; medical imaging; document image processing; soft computing; bioinformatics and computational biology; and social media mining.

This book constitutes the refereed conference proceedings of the 8th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2014, held in Bangalore, India, in December 2014. The 22 revised full papers were carefully reviewed and selected from 44 submissions. The papers feature a wide range of topics covering both theory, methods and tools as well as their diverse applications in numerous domains.

This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current topics - neural networks, support vector machines and decision trees - attributed to the recent vast progress in this field are also dealt with. Introduction to Pattern Recognition and Machine Learning will equip readers, especially senior computer science undergraduates, with a deeper understanding of the subject matter.