Data Compression for Data Mining Algorithms

Chapter 1: Overview and Contributions

• • 1.1 Overview
• • 1.2 Introduction
• • 1.3 Developments in Data Compression Techniques for Data Mining Algorithm Design
• • 1.4 Overview of the Book
• • 1.5 Contributions
• • 1.6 Conclusions

Chapter 2: Introduction to Data Mining Algorithms

• • 2.1 Introduction
• • 2.2 Association Rule Mining
• • 2.2.1 Frequent Itemsets
• • 2.2.2 Association Rules
• • 2.3 Classification
• • 2.3.1 Decision Tree
• • 2.3.2 Support Vector Machine
• • 2.4 Clustering
• • 2.4.1 k-Means Algorithm
• • 2.4.2 Single-Link Algorithm
• • 2.4.3 DBSCAN Algorithm
• • 2.4.4 Minimum Spanning Tree Algorithm
• • 2.5 Outlier Detection
• • 2.5.1 Probability Based Algorithm
• • 2.5.2 Proximity Based Algorithm
• • 2.5.3 Classification Based Algorithm
• • 2.5.4 Clustering Based Algorithm
• • 2.6 Mining Large Datasets
• • 2.6.1 Overview
• • 2.6.2 Issues and Challenges
• • 2.7 Summary
• • 2.8 Bibliographies

Chapter 3: Introduction to Data Compression Methods

• • 3.1 Feature Extraction and Data Representation
• • 3.2 Lossless Data Compression Methods
• • 3.2.1 Huffman Coding
• • 3.2.2 Arithmetic Coding
• • 3.2.3 Run-length Coding
• • 3.3 Lossy Data Compression Methods
• • 3.3.1 Quantization
• • 3.3.2 Dictionary Techniques
• • 3.3.3 Differential Encoding
• • 3.3.4 Transform Coding, Subband Coding, and Wavelets
• • 3.4 Data Compression for Data Preprocessing
• • 3.4.1 Data Reduction and Transformation
• • 3.4.2 Sampling
• • 3.4.3 Dimensionality Reduction
• • 3.5 Summary
• • 3.6 Bibliographic Notes

Chapter 4: Huffman Coding for Association Rule Mining

• • 4.1 Introduction
• • 4.2 Frequent Itemset and Association Rule Mining
• • 4.3 The Apriori Algorithm
• • 4.4 The FP-tree Algorithm
• • 4.5 The Proposed Huffman Coding for Frequent Itemset Mining
• • 4.6 Experiments and Results
• • 4.7 Conclusions
• • 4.8 References

Chapter 5: Arithmetic Coding for Maximal Frequent Itemsets Mining

• • 5.1 Introduction
• • 5.2 Maximal Frequent Itemsets Mining
• • 5.3 Arithmetic Coding
• • 5.4 The Proposed Arithmetic Coding for Maximal Frequent Itemset Mining
• • 5.5 Experiments and Results
• • 5.6 Conclusions
• • 5.7 References

Chapter 6: Feature Subset Selection for Decision Tree Construction

• • 6.1 Introduction
• • 6.2 Decision Tree for Classification
• • 6.3 Feature Subset Selection
• • 6.4 The Proposed Feature Subset Selection for Decision Tree Construction
• • 6.5 Experiments and Results
• • 6.6 Conclusions
• • 6.7 References

Chapter 7: Neural Networks for Decision Tree Construction

• • 7.1 Introduction
• • 7.2 Neural Networks
• • 7.3 Deep Neural Networks
• • 7.4 The Proposed NN-Based Feature Subset Selection for Decision Tree Construction
• • 7.5 Experiments and Results
• • 7.6 Conclusions
• • 7.7 References

Chapter 8: Principal Component Analysis for Decision Tree Construction

• • 8.1 Introduction
• • 8.2 Principal Component Analysis
• • 8.3 The Proposed PCA-Based Decision Tree Construction
• • 8.4 Experiments and Results
• • 8.5 Conclusions
• • 8.6 References

Chapter 9: Dictionary Techniques for Support Vector Machine

• • 9.1 Introduction
• • 9.2 Support Vector Machine for Classification
• • 9.3 Dictionary Techniques
• • 9.4 The Proposed Dictionary Techniques for Support Vector Machine
• • 9.5 Experiments and Results
• • 9.6 Conclusions
• • 9.7 References

Chapter 10: Quantization for Support Vector Machine

• • 10.1 Introduction
• • 10.2 Scalar Quantization
• • 10.3 Vector Quantization
• • 10.4 The Proposed Quantization Method for Support Vector Machine
• • 10.5 Experiments and Results
• • 10.6 Conclusions
• • 10.7 References

Chapter 11: A Sparse Data Representation for Clustering

• • 11.1 Introduction
• • 11.2 Background
• • 11.3 The Proposed Data Compression Method
• • 11.4 Experiments and Results
• • 11.5 Conclusions
• • 11.6 References

Chapter 12: Dictionary Coding Based Compression for Clustering

• • 12.1 Introduction
• • 12.2 Background
• • 12.3 The Proposed Dictionary Coding Method for Efficient Clustering
• • 12.4 Experiments and Results
• • 12.5 Conclusions
• • 12.6 References

Chapter 13: Nearest Neighbor Based Compression for Outlier Detection

• • 13.1 Introduction
• • 13.2 Background
• • 13.3 The Proposed Data Compression Method for Efficient Outlier Detection
• • 13.4 Experiments and Results
• • 13.5 Conclusions
• • 13.6 References

Chapter 14: Huffman Coding for Outlier Detection

• • 14.1 Introduction
• • 14.2 Background
• • 14.3 The Proposed Multi-dimensional Data Compression by Huffman Coding
• • 14.4 Experiments and Results
• • 14.5 Conclusions
• • 14.6 References

Chapter 15: Arithmetic Coding for Outlier Detection

• • 15.1 Introduction
• • 15.2 Background
• • 15.3 The Proposed Multi-dimensional Data Compression by Arithmetic Coding
• • 15.4 Experiments and Results
• • 15.5 Conclusions
• • 15.6 References