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Data Mining and Knowledge Discovery Handbook

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"Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository.
Introduction to knowledge discovery in databases.- Part I Preprocessing methods.- Data cleansing.- Handling missing attribute values.- Geometric methods for feature extraction and dimensional reduction.- Dimension Reduction and feature selection.- Discretization methods.- outlier detection.- Part II Supervised methods.- Introduction to supervised methods.- Decision trees.- Bayesian networks.- Data mining within a regression framework.- Support vector machines.- Rule induction.- Part III Unsupervised methods.- Visualization and data mining for high dimensional datasets.- Clustering methods.- Association rules.- Frequent set mining.- Constraint-based data mining.- Link analysis.- Part IV Soft computing methods.- Evolutionary algorithms for data mining.- Reinforcement-learning: an overview from a data mining perspective.- Neural networks.- On the use of fuzzy logic in data mining.- Granular computing and rough sets.- Part V Supporting methods.- Statistical methods for data mining.- Logics for data mining.- Wavelet methods in data mining.- Fractal mining.- Interestingness measures.- Quality assessment approaches in data mining.- Data mining model comparison.- Data mining query languages.- Part VI Advanced methods.- Meta-learning.- Bias vs variance decomposition for regression and classification.- Mining with rare cases.- Mining data streams.- Mining high-dimensional data.- Text mining and information extraction.- Spatial data mining.- Data mining for imbalanced datasets: an overview.- Relational data mining.- Web mining.- A review of web document clustering approaches.- Causal discovery.- Ensemble methods for classifiers.- Decomposition methodology for knowledge discovery and data mining.- Information fusion.- Parallel and grid-based data mining.- Collaborative data mining.- Organizational data mining.- Mining time series data.- Part VII Applications.- Data mining in medicine.- Learning information patterns in biological databases.- Data mining for selection of manufacturing processes.- Data mining of design products and processes.- Data mining in telecommunications.- Data mining for financial applications.- Data mining for intrusion detection.- Data mining for software testing.- Data mining for CRM.- Data mining for target marketing.- Part VIII Software.- Oracle data mining.- Building data mining solutions with OLE DB for DM and XML for analysis.- LERS-A data mining system.- GainSmarts data mining system for marketing.- WizSoft's WizWhy.- DataEngine.- Index.

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