Data Mining: Foundations and Intelligent Paradigms: Volume 3: Medical, Health, Social, Biological and other Applications (Intelligent Systems Reference Library, 25)

✍ Scribed by Dawn E. Holmes (editor), Lakhmi C Jain (editor)

• Publisher: Springer
• Year: 2012
• Tongue: English
• Leaves: 367
• Category: Library

✦ Synopsis

There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 3: Medical, Health, Social, Biological and other Applications” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

✦ Table of Contents

Title Page
Preface
Contents
Editors
Advances in Intelligent Data Mining
Introduction
Medical Influences
Health Influences
Social Influences
Information Discovery
On-Line Communities
Biological Influences
Biological Networks
Estimations in Gene Expression
Chapters Included in the Book
Conclusion
References
Temporal Pattern Mining for Medical Applications
Introduction
Types of Temporal Data in Medical Domain
Definitions
Temporal Pattern Mining Algorithms
Temporal Pattern Mining from a Set of Sequences
Temporal Pattern Mining from a Single Sequence
Medical Applications
Conclusions
References
BioKeySpotter: An Unsupervised Keyphrase Extraction
Technique in the Biomedical Full-Text Collection
Introduction
Backgrounds and Related Work
The Proposed Approach
Evaluation
Dataset
Comparison Algorithms
Experimental Results
Conclusion
References
Mining Health Claims Data for Assessing Patient Risk
What Is Health Risk?
Traditional Models for Assessing Health Risk
Risk Factor-Based Risk Models
Data Sources
Enrollment Data
Claims and Coding Systems
Interpretation of Claims Codes
Clinical Identification Algorithms
Sensitivity-Specificity Trade-Off
Constructing an Identification Algorithm
Sources of Algorithms
Construction and Use of Grouper Models
Drug Grouper Models
Drug-Based Risk Adjustment Models
Summary and Conclusions
References
Mining Biological Networks for Similar Patterns
Introduction
Metabolic Network Alignment with One-to-One Mappings
Model
Problem Formulation
Pairwise Similarity of Entities
Similarity of Topologies
Combining Homology and Topology
Extracting the Mapping of Entities
Similarity Score of Networks
Complexity Analysis
Metabolic Network Alignment with One-to-Many Mappings
Homological Similarity of Subnetworks
Topological Similarity of Subnetworks
Combining Homology and Topology
Extracting Subnetwork Mappings
Significance of Network Alignment
Identification of Alternative Entities
Identification of Alternative Subnetworks
One-to-Many Mappings within and across Major Clades
Summary
Further Reading
References
Estimation of Distribution Algorithms in Gene
Expression Data Analysis
Introduction
Estimation of Distribution of Algorithms
Model Building in EDA
Notation
Models with Independent Variables
Models with Pair Wise Dependencies
Models with Multiple Dependencies
Application of EDA in Gene Expression Data Analysis
State-of-Art of the Application of EDAs in Gene Expression Data Analysis
Conclusion
References
Gene Function Prediction and Functional
Network: The Role of Gene Ontology
Introduction
Gene Function Prediction
Functional Gene Network Generation
Related Work and Limitations
GO-Based Gene Similarity Measures
Estimating Support for PPI Data with Applications to Function Prediction
Mixture Model of PPI Data
Data Sets
Function Prediction
Evaluating the Function Prediction
Experimental Results
Discussion
A Functional Network of Yeast Genes Using Gene Ontology Information
Data Sets
Constructing a Functional Gene Network
Using Semantic Similarity (SS)
Evaluating the Functional Gene Network
Experimental Results
Discussion
Conclusions
References
Mining Multiple Biological Data for Reconstructing
Signal Transduction Networks
Introduction
Background
Signal Transduction Network
Protein-Protein Interaction
Constructing Signal Transduction Networks Using Multiple Data
Related Work
Materials and Methods
Clustering and Protein-Protein Interaction Networks
Evaluation
Some Results of Yeast STN Reconstruction
Outlook
Summary
References
Mining Epistatic Interactions from
High-Dimensional Data Sets
Introduction
Background
Epistasis
Detecting Epistasis
High-Dimensional Data Sets
Barriers to Learning Epistasis
MDR
Bayesian Networks
Discovering Epistasis Using Bayesian Networks
A Bayesian Network Model for Epistatic Interactions
The BNMBL Score
Experiments
Efficient Search
Experiments
Discussion, Limitations, and Future Research
References
Knowledge Discovery in Adversarial Settings
Introduction
Characteristics of Adversarial Modelling
Technical Implications
Conclusion
References
Analysis and Mining of Online Communities of
Internet Forum Users
Introduction
What Is Web 2.0?
New Forms of Participation — Push or Pull?
Internet Forums as New Forms of Conversation
Social-Driven Data
What Are Social-Driven Data?
Data from Internet Forums
Internet Forums
Crawling Internet Forums
Statistical Analysis
Index Analysis
Network Analysis
Related Work
Conclusions
References
Data Mining for Information Literacy
Introduction
Background
Information Literacy
Critical Literacy
Educational Data Mining
Towards Critical Data Literacy: A Frame for Analysis and Design
A Frame of Analysis: Technique and Object
On the Chances of Achieving Critical Data Literacy: Principles of Successful Learning as Description Criteria
Examples: Tools and Other Approaches Supporting Data Mining for Information Literacy
Analysing Data: Do-It-Yourself Statistics Visualization
Analysing Language: Viewpoints and Bias in Media Reporting
Analysing Data Mining: Building, Comparing and Re-using Own and Others' Conceptualizations of a Domain
Analysing Actions: Feedback and Awareness Tools
Analysing Actions: Role Reversals in Data Collection and Analysis
Summary and Conclusions
References
Rule Extraction from Neural Networks and
Support Vector Machines for Credit Scoring
Introduction
Re-RX: Recursive Rule Extraction from Neural Networks
Multilayer Perceptron
Finding Optimal Network Structure by Pruning
Recursive Rule Extraction
Applying Re-RX for Credit Scoring
ALBA: Rule Extraction from Support Vector Machines
Support Vector Machine
ALBA: Active Learning Based Approach to SVM Rule Extraction
Applying ALBA for Credit Scoring
Conclusion
References
Using Self-Organizing Map for Data Mining:
A Synthesis with Accounting Applications
Introduction
Data Pre-processing
Types of Variables
Distance Metrics
Rescaling Input Variables
Self-Organizing Map
Introduction to SOM
Formation of SOM
Performance Metrics and Cluster Validity
Extensions of SOM
Non-metric Spaces
SOM for Temporal Sequence Processing
SOM for Cluster Analysis
SOM for Visualizing High-Dimensional Data
Financial Applications of SOM
Case Study: Clustering Accounting Databases
Data Description
Data Pre-processing
Experiments
Results Presentation and Discussion
References
Applying Data Mining Techniques to Assess Steel Plant
Operation Conditions
Introduction
Brief Description of EAF
Performance Evaluation Criteria
Innovations in Electric Arc Furnaces
Details of the Operation
Understanding SCIPs and Stages of a $Heat$
Problem Description
Data Mining Process
Data
Data Preprocessing
Attribute Pruning
The Experiments
Data Mining Techniques
Results
Discussion
Concluding Remarks
References
Author Index