Methodologies Knowledge Discovery Data (43 results)

X, 303 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Stamped. Studies in Big Data, Vol. 1. Sprache: Englisch. …

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Invited Talks.- KDD as an Enterprise IT Tool: Reality and Agenda.- Computer Assisted Discovery of First Principle Equations from Numeric Data.- Emerging KDD Technology.- Data Mining - a Rough Set Perspective.- Data Mining Techniques for Associations, Clustering and Classification.- Data Mining: Granular Computing Approach.- Rule Extraction from Prediction Models.- Association Rules.- Mining Association Rules on Related Numeric Attributes.- LGen - A Lattice-Based Candidate Set Generation Algorithm for I/O Efficient Association Rule Mining.- Extending the Applicability of Association Rules.- An Efficient Approach for Incremental Association Rule Mining.- Association Rules in Incomplete Databases.- Parallel SQL Based Association Rule Mining on Large Scale PC Cluster: Performance Comparison with Directly Coded C Implementation.- H-Rule Mining in Heterogeneous Databases.- An Improved Definition of Multidimensional Inter-transaction Association Rule.- Incremental Discovering Association Rules: A Concept Lattice Approach.- Feature Selection and Generation.- Induction as Pre-processing.- Stochastic Attribute Selection Committees with Multiple Boosting: Learning More Accurate and More Stable Classifier Committees.- On Information-Theoretic Measures of Attribute Importance.- A Technique of Dynamic Feature Selection Using the Feature Group Mutual Information.- A Data Pre-processing Method Using Association Rules of Attributes for Improving Decision Tree.- Mining in Semi, Un-structured Data.- An Algorithm for Constrained Association Rule Mining in Semi-structured Data.- Incremental Mining of Schema for Semistructured Data.- Discovering Structure from Document Databases.- Combining Forecasts from Multiple Textual Data Sources.- Domain Knowledge Extracting in a Chinese NaturalLanguage Interface to Databases: NChiql.- Interestingness, Surprisingness, and Exceptions.- Evolutionary Hot Spots Data Mining.- Efficient Search of Reliable Exceptions.- Heuristics for Ranking the Interestingness of Discovered Knowledge.- Rough Sets, Fuzzy Logic, and Neural Networks.- Automated Discovery of Plausible Rules Based on Rough Sets and Rough Inclusion.- Discernibility System in Rough Sets.- Automatic Labeling of Self-Organizing Maps: Making a Treasure-Map Reveal Its Secrets.- Neural Network Based Classifiers for a Vast Amount of Data.- Accuracy Tuning on Combinatorial Neural Model.- A Situated Information Articulation Neural Network: VSF Network.- Neural Method for Detection of Complex Patterns in Databases.- Preserve Discovered Linguistic Patterns Valid in Volatility Data Environment.- An Induction Algorithm Based on Fuzzy Logic Programming.- Rule Discovery in Databases with Missing Values Based on Rough Set Model.- Sustainability Knowledge Mining from Human Development Database.- Induction, Classification, and Clustering.- Characterization of Default Knowledge in Ripple Down Rules Method.- Improving the Performance of Boosting for Naive Bayesian Classification.- Convex Hulls in Concept Induction.- Mining Classification Knowledge Based on Cloud Models.- Robust Clusterin of Large Geo-referenced Data Sets.- A Fast Algorithm for Density-Based Clustering in Large Database.- A Lazy Model-Based Algorithm for On-Line Classification.- An Efficient Space-Partitioning Based Algorithm for the K-Means Clustering.- A Fast Clustering Process for Outliers and Remainder Clusters.- Optimising the Distance Metric in the Nearest Neighbour Algorithm on a Real-World Patient Classification Problem.- Classifying Unseen Cases with Many Missing Values.- Study of a Mixed SimilarityMeasure for Classification and Clustering.- Visualization.- Visually Aided Exploration of Interesting Association Rules.- DVIZ: A System for Visualizing Data Mining.- Causal Model and Graph-Based Methods.- A Minimal Causal Model Learner.- Efficient Graph-Based Algorithm for Discovering and Maintaining Knowledge in Large Databases.- Basket Analysis for Graph Structured Data.- The Evolution of Causal Mo.…

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Taschenbuch. Condition: Neu. Knowledge Discovery Using Data mining and Soft Computing Methodologies | Research oriented book | Dharmpal Singh | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786207807079 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. …

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Condition: Sehr gut. Zustand: Sehr gut | Seiten: 316 | Sprache: Englisch | Produktart: Bücher | The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.…

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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.…

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Condition: gut. 2011. Mining Software Specifications: Methodologies and Applications (Chapman & Hall/Crc Data Mining and Knowledge Discovery, Band 17) In englischer Sprache. pages.

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