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Data compression (Computer science) --- Compression of data (Computer science) --- Compression of files (Computer science) --- Compression of text (Computer science) --- File compression (Computer science) --- Text compression (Computer science) --- Coding theory --- Database management --- File organization (Computer science) --- Data compression (Telecommunication)
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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.
Artificial intelligence. --- Computer science. --- Data mining. --- Optical pattern recognition. --- Engineering & Applied Sciences --- Electrical & Computer Engineering --- Electrical Engineering --- Computer Science --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Informatics --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Pattern recognition. --- Computer Science. --- Pattern Recognition. --- Data Mining and Knowledge Discovery. --- Artificial Intelligence (incl. Robotics). --- Database searching --- Science --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Artificial Intelligence. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Data compression (Computer science) --- Image processing. --- Pattern perception.
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This book discusses compressive sensing in the presence of side information. Compressive sensing is an emerging technique for efficiently acquiring and reconstructing a signal. Interesting instances of Compressive Sensing (CS) can occur when, apart from sparsity, side information is available about the source signals. The side information can be about the source structure, distribution, etc. Such cases can be viewed as extensions of the classical CS. In these cases we are interested in incorporating the side information to either improve the quality of the source reconstruction or decrease the number of samples required for accurate reconstruction. In this book we assume availability of side information about the feasible region. The main applications investigated are image deblurring for optical imaging, 3D surface reconstruction, and reconstructing spatiotemporally correlated sources. The author shows that the side information can be used to improve the quality of the reconstruction compared to the classic compressive sensing. The book will be of interest to all researchers working on compressive sensing, inverse problems, and image processing.
Engineering & Applied Sciences --- Electrical & Computer Engineering --- Electrical Engineering --- Technology - General --- Applied Physics --- Coding theory. --- Data compression (Telecommunication) --- Signal processing --- Sampling (Statistics) --- Digital techniques. --- Random sampling --- Statistics of sampling --- Digital signal processing --- Compression of data (Telecommunication) --- Computer science. --- Computer graphics. --- Computer mathematics. --- Statistical physics. --- Dynamical systems. --- Computer Science. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Signal, Image and Speech Processing. --- Statistical Physics, Dynamical Systems and Complexity. --- Computational Science and Engineering. --- Statistics --- Mathematical statistics --- Digital communications --- Digital electronics --- Data compression (Computer science) --- Data transmission systems --- Information theory --- Machine theory --- Signal theory (Telecommunication) --- Computer programming --- Computer vision. --- Complex Systems. --- Statistical Physics and Dynamical Systems. --- Physics --- Informatics --- Science --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Statistical methods --- Optical data processing. --- Signal processing. --- Image processing. --- Speech processing systems. --- Computer mathematics --- Electronic data processing --- Mathematics --- Dynamical systems --- Kinetics --- Mechanics, Analytic --- Force and energy --- Mechanics --- Statics --- Computational linguistics --- Electronic systems --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Processing, Signal --- Information measurement --- Optical computing --- Visual data processing --- Bionics --- Integrated optics --- Photonics --- Computers --- Optical equipment
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