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Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection. Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview. The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas.

Cluster analysis. --- Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Correlation (Statistics) --- Multivariate analysis --- Spatial analysis (Statistics) --- Data structures (Computer scienc. --- Information storage and retrieva. --- Mathematical statistics. --- Computer science. --- Optical pattern recognition. --- Data Structures and Information Theory. --- Information Storage and Retrieval. --- Statistical Theory and Methods. --- Math Applications in Computer Science. --- Statistics and Computing/Statistics Programs. --- Pattern Recognition. --- Data structures (Computer science) --- Information storage and retrieval. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Informatics --- Science --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- Electronic data processing --- File organization (Computer science) --- Abstract data types (Computer science) --- Statistical methods --- Information storage and retrieval systems. --- Automatic data storage --- Automatic information retrieval --- Automation in documentation --- Computer-based information systems --- Data processing systems --- Data storage and retrieval systems --- Discovery systems, Information --- Information discovery systems --- Information processing systems --- Information retrieval systems --- Machine data storage and retrieval --- Mechanized information storage and retrieval systems --- Computer systems --- Electronic information resources --- Data libraries --- Digital libraries --- Information organization --- Information retrieval --- Data structures (Computer science). --- Statistics . --- Computer science—Mathematics. --- Pattern recognition. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics

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• Reference Manager
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• Reference Manager
• EndNote
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