Шалилех Соруш Ахмад Кластеризация в обогащенных признаками сетях с использованием подхода восстановления данных

Короткий опис

Шалилех Соруш Ахмад Кластеризация в обогащенных признаками сетях с использованием подхода восстановления данных
ОГЛАВЛЕНИЕ ДИССЕРТАЦИИ
кандидат наук Шалилех Соруш Ахмад
Contents

Abstract ii

Acknowledgements iii

1 Introduction

1.1 The relevance and importance of research

1.2 Novelty of the obtained results

1.3 Publications and approbation of the research

1.4 The organization of thesis

2 Literature review

2.1 Network-only clustering methods

2.2 Early fusion methods

2.3 Simultaneous fusion methods

2.4 Late fusion methods

3 Methodologies

3.1 Sequential data recovery clusters extraction methods

3.1.1 Sequential methods at feature-rich networks

3.1.1.1 Motivation

3.1.1.2 Notation

3.1.1.3 Methodology

3.1.2 Sequential methods at feature-rich networks using similarity data

3.1.2.1 Motivation

3.1.2.2 Inner products as similarities

3.1.2.3 Notation

3.1.2.4 Methodology

3.2 Simultaneous data recovery clusters extraction methods

3.2.1 Simultaneous methods at feature-rich network

3.2.1.1 Motivation

3.2.1.2 Notation

3.2.1.3 Methodology

4 Experimental setting

4.1 Algorithms under comparison

4.2 Data sets

4.2.1 Real world data sets

4.2.2 Generating synthetic data sets

4.3 Data pre-processing techniques

4.4 Evaluation criteria

5 Experiments

5.1 Experimental comparison of the methods under consideration

5.1.1 Comparison of the methods over real-world data sets

5.1.2 Comparison of the methods over synthetic data sets with categorical features

5.1.3 Execution time of the methods under consideration over synthetic data

sets with categorical features

5.2 Experimental validation of the proposed methods

5.2.1 Choosing the data standardization options

5.2.1.1 Investigating the impact data pre-processing techniques at small-size networks with quantitative features

5.2.1.2 Investigating the impact data pre-processing techniques at small-size networks with categorical features

5.2.1.3 Investigating the impact data pre-processing techniques at small-size networks combination of quantitative and categorical features

5.2.1.4 Conclusion on pre-processing techniques

5.2.2 Experimental results for the proposed methods at various feature scales

5.2.2.1 The proposed methods at synthetic networks with quantitative features at the nodes

5.2.2.2 The proposed methods at synthetic networks with categorical features at the nodes

5.2.2.3 The proposed methods at synthetic networks with combination of quantitative and categorical features at the nodes

6 Conclusions & future work

6.1 Conclusion

6.2 Future works

Bibliography

A Appendix

.1 The sequential clusters extraction at similarity data: notation of all modes

.2 The sequential clusters extraction at similarity data: methodology of all modes

B Appendix

List of Figures

List of Tables

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