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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques

Contributor(s): Triantaphyllou, Evangelos [editor.] | Felici, Giovanni [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Massive Computing: 6Publisher: Boston, MA : Springer US, 2006.Description: XLVIII, 748 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780387342962.Subject(s): Computer science | Operations research | Decision making | Information storage and retrieval | Artificial intelligence | Management science | Computer Science | Artificial Intelligence (incl. Robotics) | Information Storage and Retrieval | Operations Research, Management Science | Operation Research/Decision TheoryDDC classification: 006.3 Online resources: Click here to access online
Contents:
A Common Logic Approach to Data Mining and Pattern Recognition -- The One Clause at a Time (OCAT) Approach to Data Mining and Knowledge Discovery -- An Incremental Learning Algorithm for Inferring Logical Rules from Examples in the Framework of the Common Reasoning Process -- Discovering Rules That Govern Monotone Phenomena -- Learning Logic Formulas and Related Error Distributions -- Feature Selection for Data Mining -- Transformation of Rational Data and Set Data to Logic Data -- Data Farming: Concepts and Methods -- Rule Induction Through Discrete Support Vector Decision Trees -- Multi-Attribute Decision Trees and Decision Rules -- Knowledge Acquisition and Uncertainty in Fault Diagnosis: A Rough Sets Perspective -- Discovering Knowledge Nuggets with a Genetic Algorithm -- Diversity Mechanisms in Pitt-Style Evolutionary Classifier Systems -- Fuzzy Logic in Discovering Association Rules: An Overview -- Mining Human Interpretable Knowledge with Fuzzy Modeling Methods: An Overview -- Data Mining from Multimedia Patient Records -- Learning to Find Context Based Spelling Errors -- Induction and Inference with Fuzzy Rules for Textual Information Retrieval -- Statistical Rule Induction in the Presence of Prior Information: The Bayesian Record Linkage Problem -- Some Future Trends in Data Mining.
In: Springer eBooksSummary: This book will give the reader a perspective into the core theory and practice of data mining and knowledge discovery (DM&KD). Its chapters combine many theoretical foundations for various DM&KD methods, and they present a rich array of examples—many of which are drawn from real-life applications. Most of the theoretical developments discussed are accompanied by an extensive empirical analysis, which should give the reader both a deep theoretical and practical insight into the subjects covered. The book presents the combined research experiences of its 40 authors gathered during a long search in gleaning new knowledge from data. The last page of each chapter has a brief biographical statement of its contributors, who are world-renowned experts. Audience The intended audience for this book includes graduate students studying data mining who have some background in mathematical logic and discrete optimization, as well as researchers and practitioners in the same area.
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A Common Logic Approach to Data Mining and Pattern Recognition -- The One Clause at a Time (OCAT) Approach to Data Mining and Knowledge Discovery -- An Incremental Learning Algorithm for Inferring Logical Rules from Examples in the Framework of the Common Reasoning Process -- Discovering Rules That Govern Monotone Phenomena -- Learning Logic Formulas and Related Error Distributions -- Feature Selection for Data Mining -- Transformation of Rational Data and Set Data to Logic Data -- Data Farming: Concepts and Methods -- Rule Induction Through Discrete Support Vector Decision Trees -- Multi-Attribute Decision Trees and Decision Rules -- Knowledge Acquisition and Uncertainty in Fault Diagnosis: A Rough Sets Perspective -- Discovering Knowledge Nuggets with a Genetic Algorithm -- Diversity Mechanisms in Pitt-Style Evolutionary Classifier Systems -- Fuzzy Logic in Discovering Association Rules: An Overview -- Mining Human Interpretable Knowledge with Fuzzy Modeling Methods: An Overview -- Data Mining from Multimedia Patient Records -- Learning to Find Context Based Spelling Errors -- Induction and Inference with Fuzzy Rules for Textual Information Retrieval -- Statistical Rule Induction in the Presence of Prior Information: The Bayesian Record Linkage Problem -- Some Future Trends in Data Mining.

This book will give the reader a perspective into the core theory and practice of data mining and knowledge discovery (DM&KD). Its chapters combine many theoretical foundations for various DM&KD methods, and they present a rich array of examples—many of which are drawn from real-life applications. Most of the theoretical developments discussed are accompanied by an extensive empirical analysis, which should give the reader both a deep theoretical and practical insight into the subjects covered. The book presents the combined research experiences of its 40 authors gathered during a long search in gleaning new knowledge from data. The last page of each chapter has a brief biographical statement of its contributors, who are world-renowned experts. Audience The intended audience for this book includes graduate students studying data mining who have some background in mathematical logic and discrete optimization, as well as researchers and practitioners in the same area.

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