How Can a Loss of Information in Mixed Attribute Datasets be Prevented?

On the Imputation of Missing Values in Mixed Attribute Datasets Using Higher Order Kernel Functions


Master's Thesis, 2012

43 Pages, Grade: 1.00


Excerpt


Table of Contents

CHAPTER 1: INTRODUCTION
1.1 Objective of the work
1.2 Introduction to data mining
1.3 Missing values
1.4 Missing value imputation
1.5 Model flow diagram
1.6 Organizationof the report
1.7 Summary

CHAPTER 2: LITERATURE REVIEW
2.1 Introduction
2.2 Literature review
2.2.1 Missing values
2.2.2 Missing value imputation
2.2.3 Kernel functions
2.3 Summary

CHAPTER 3: DATASET DESCRIPTION
3.1 Introduction
3.2 Data set description
3.3 Summary

CHAPTER 4: IMPUTATION TECHNIQUES
4.1 Introduction
4.2 K –Nearest neighbor imputation method
4.3 Experimental results for imputation done using K-NN
4.4 Frequency Estimation Method
4.5 Experimental results for frequency estimator
4.6 Kernel Functions
4.7 Imputation using RBF kernel
4.8 Experimental results for rbf kernel
4.9 Imputation using poly kernel
4.10 Experimental results for poly kernel
4.11 Summary

CHAPTER 5: IMPUTATION USING MIXTURE OF KERNELS
5.1 Introduction
5.2 Interpolation and Extrapolation
5.3 Mixture of kernels
5.4 Experimental results for mixture of kernels
5.5 Imputation using spherical kernel with rbf kernel
5.6 Experimental results for imputation using spherical kernel and rbf kernel
5.7 Imputation using spherical kernel and poly kernel
5.8 Experimental results for spherical kernel and poly kernel
5.9 Summary

CHAPTER 6: RESULTS AND DISCUSSION
6.1 Introduction
6.2 Performance evaluation
6.3 Experimental results and discussion
6.4 Discussion of results
6.5 Summary

CHAPTER 7: CONCLUSION AND FUTURE WORK
7.1 Conclusion
7.2 Future work

REFERENCES

APPENDIX-A

APPENDIX-B

Excerpt out of 43 pages

Details

Title
How Can a Loss of Information in Mixed Attribute Datasets be Prevented?
Subtitle
On the Imputation of Missing Values in Mixed Attribute Datasets Using Higher Order Kernel Functions
College
Avinashilingam University
Grade
1.00
Author
Year
2012
Pages
43
Catalog Number
V457847
ISBN (eBook)
9783668905337
ISBN (Book)
9783668905344
Language
English
Keywords
loss, information, mixed, attribute, datasets, prevented, imputation, missing, values, using, higher, order, kernel, functions
Quote paper
Aasha Ajith (Author), 2012, How Can a Loss of Information in Mixed Attribute Datasets be Prevented?, Munich, GRIN Verlag, https://www.grin.com/document/457847

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