An Information Based Measure for Grouping Quality 1st edition by Erik Engbers, Michael Lindenbaum, Arnold Smeulders ISBN 3540219828 9783540219828

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Product details:

ISBN 10:       3540219828
ISBN 13: 978-3540219828
Author:         Erik A. Engbers, Michael Lindenbaum, Arnold W. M. Smeulders
 

An Information-Based Measure for Grouping Quality 1st Table of contents:

• Introduction

  • 1.1 Background and Motivation
  • 1.2 Grouping and Clustering in Data Analysis
  • 1.3 Need for Quantitative Grouping Quality Measures
  • 1.4 Information Theory in Grouping and Clustering
  • 1.5 Objectives and Contributions of the Paper
  • 1.6 Organization of the Paper

• Related Work

  • 2.1 Overview of Grouping and Clustering Metrics
  • 2.2 Information-Theoretic Approaches in Clustering Evaluation
  • 2.3 Existing Measures for Grouping Quality
  • 2.4 Comparison of Information-Based Metrics with Other Approaches
  • 2.5 Limitations of Current Methods and Gaps in the Literature

• Preliminaries and Background

  • 3.1 Basic Concepts in Information Theory
  • 3.2 Entropy and Mutual Information
  • 3.3 Grouping and Partitioning of Data
  • 3.4 Common Grouping Quality Measures (e.g., purity, compactness)
  • 3.5 The Role of Uncertainty in Grouping Quality

• Information-Based Measure for Grouping Quality

  • 4.1 Conceptual Framework of the Information-Based Measure
  • 4.2 Definition of the Proposed Quality Measure
  • 4.3 Relation to Entropy, Information Gain, and Mutual Information
  • 4.4 Incorporating Data Structure and Distribution into the Measure
  • 4.5 Properties of the Measure: Consistency, Sensitivity, and Interpretability

• Algorithmic Implementation

  • 5.1 Overview of the Algorithm for Computing the Measure
  • 5.2 Steps for Evaluating Grouping Quality in Practice
  • 5.3 Computational Complexity and Scalability Considerations
  • 5.4 Handling Different Types of Data (e.g., categorical, continuous)
  • 5.5 Extensions to Multidimensional Data and Multi-Label Grouping

• Experimental Setup and Evaluation

  • 6.1 Datasets for Grouping and Clustering Tasks
  • 6.2 Benchmarking Against Standard Clustering Evaluation Methods
  • 6.3 Evaluation Metrics: Comparing Grouping Consistency, Compactness, and Separation
  • 6.4 Cross-validation and Experimental Protocols
  • 6.5 Testing the Robustness of the Measure in Noisy or Unbalanced Data

• Results and Discussion

  • 7.1 Visual and Quantitative Results of Grouping Quality Assessment
  • 7.2 Performance Comparison with Other Grouping Quality Metrics
  • 7.3 Analysis of the Measure’s Sensitivity to Grouping Structure
  • 7.4 Effects of Varying Data Complexity on Grouping Quality
  • 7.5 Discussion on Practical Implications and Insights

• Applications of the Information-Based Measure

  • 8.1 Clustering in Machine Learning and Data Mining
  • 8.2 Grouping for Image Segmentation and Object Recognition
  • 8.3 Analysis of Social Networks and Community Detection
  • 8.4 Topic Modeling and Text Clustering
  • 8.5 Applications in Bioinformatics and Genomic Data Analysis

• Challenges and Future Directions

  • 9.1 Generalizing the Measure to Complex and Heterogeneous Data
  • 9.2 Incorporating Temporal and Sequential Aspects in Grouping
  • 9.3 Handling Large-Scale Data and Scalability Challenges
  • 9.4 Extensions to Overlapping or Fuzzy Clusters
  • 9.5 Open Problems and Future Research in Grouping Evaluation

• Conclusion

  • 10.1 Summary of Contributions and Findings
  • 10.2 Impact of the Information-Based Measure on Grouping Evaluation
  • 10.3 Limitations and Potential Improvements
  • 10.4 Future Work and Open Research Questions

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