基于不确定性建模的数据挖掘（英文版） PDF 高清完整版

基于不确定性建模的数据挖掘（英文版）

作者：秦曾昌，汤永川 著

出版时间：2013年版

《基于不确定性建模的数据挖掘（英文版）》的英文简介如下： Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise,incomplete or noisy. Uncertainty Modeling for Data Mining A Label Semantics Approach introduces label semantics, a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing anduncertainty reasoning. 《基于不确定性建模的数据挖掘（英文版）》由秦曾昌、汤永川著。

目录

• 1Introduction
• 1.1Types of Uncertainty
• 1.2Uncertainty Modeling and Data Mining
• 1.3Related Works
• References
• 2Induction and Learning
• 2.1Introduction
• 2.2Machine Learning
• 2.2.1Searching in Hypothesis Space
• 2.2.2Supervised Learning
• 2.2.3Unsupervised Learning
• 2.2.4Instance-Based Learning
• 2.3Data Mining and Algorithms
• 2.3.1Why Do We Need Data Mining
• 2.3.2How Do We do Data Mining
• 2.3.3Artificial Neural Networks
• 2.3.4Support Vector Machines
• 2.4Measurement of Classifiers
• 2.4.1ROC Analysis for Classification
• 2.4.2Area Under the ROC Curve
• 2.5Summary
• References
• 3Label Semantics Theory
• 3.1Uncertainty Modeling with Labels
• 3.1.1Fuzzy Logic
• 3.1.2Computing with Words
• 3.1.3Mass Assignment Theory
• 3.2Label Semantics
• 3.2.1Epistemic View of Label Semantics
• 3.2.2Random Set Framework
• 3.2.3Appropriateness Degrees
• 3.2.4Assumptions for Data Analysis
• 3.2.5Linguistic Translation
• 3.3Fuzzy Discretization
• 3.3.1Percentile-Based Discretization
• 3.3.2Entropy-Based Discretization
• 3.4Reasoning with Fuzzy Labels
• 3.4.1Conditional Distribution Given Mass Assignments
• 3.4.2Logical Expressions of Fuzzy Labels
• 3.4.3Linguistic Interpretation of Appropriate Labels
• 3.4.4Evidence Theory and Mass Assignment
• 3.5Label Relations
• 3.6Summary
• References
• 4Linguistic Decision Trees for Classification
• 4.1Introduction
• 4.2Tree Induction
• 4.2.1Entropy
• 4.2.2Soft Decision Trees
• 4.3Linguistic Decision for Classification
• 4.3.1Branch Probability
• 4.3.2Classification by LDT
• 4.3.3Linguistic ID3 Algorithm
• 4.4Experimental Studies
• 4.4.1Influence of the Threshold
• 4.4.2Overlapping Between Fuzzy Labels
• 4.5Comparison Studies
• 4.6Merging of Branches
• 4.6.1Forward Merging Algorithm
• 4.6.2Dual-Branch LDTs
• 4.6.3Experimental Studies for Forward Merging
• 4.6.4ROC Analysis for Forward Merging
• 4.7Linguistic Reasoning
• 4.7.1Linguistic Interpretation of an LDT
• 4.7.2Linguistic Constraints
• 4.7.3Classification of Fuzzy Data
• 4.8Summary
• References
• ……
• 5 Linguistic Decision Trees for Prediction
• 6 Bayesian Methods Based on Label Semantics
• 7 Unsupervised Learning with Label Semantics
• 8 Linguistic FOIL and Multiple Attribute Hierarchy for Decision Making
• 9 A prototype Theory Interpretation of Label Semantics
• 10 Prototype Theory for Learning
• 11 Prototype-Based Rule Systems
• 12 Information Cells and Information Cell Mixture Models