Мань Тяньсин Система управления базами знаний для управления процессами интеллектуального анализа данных

Краткое описание

Мань Тяньсин Система управления базами знаний для управления процессами интеллектуального анализа данных
ОГЛАВЛЕНИЕ ДИССЕРТАЦИИ
кандидат наук Мань Тяньсин
Contents

РЕФЕРАТ

Общая характеристика работы

Содержание работы

Synopsis

General thesis summary

Thesis Contents

Introduction

CHAPTER 1. Fundamentals

1.1. Introduction

1.2. Data Mining Process Models

1.3. Algorithm selection and workflow construction

1.3.1. RICE model

1.3.2. Revised RICE model

1.3.3. Requirements to the workflow construction

1.4. Semantic meta mining

1.5. Data Mining Ontologies

1.6. Efficiency indicators

1.7. Conclusions

CHAPTER 2. Meta mining framework

2.1. Introduction

2.2. Architecture of the meta mining framework

2.2.1. Analysis layer

2.2.2. Representation layer

2.2.3. Service layer

2.2.4. Application layer

2.3. Meta mining framework

2.4. Evaluation

2.5. Conclusions

CHAPTER 3. DM ontologies

3.1. Introduction

3.2. DM core ontology

3.2.1. Content of DM core ontology

3.2.2. Classes and properties of DM core ontology

3.2.3. Statistical metrics for DM core ontology

3.3. DM dataset characterization ontology

3.3.1. Data characterization for algorithm selection

3.3.2. Design of the dataset characterization ontology

3.3.3. Main Classes in dataset characterization ontology

3.3.4. Case study

3.4. DM process ontology

3.4.1. Existing DM process models and ontologies

3.4.2. Design of the DM process ontology

3.4.3. Main classes in DM process ontology

3.4.4. Case study

3.4.5. Statistical metrics for DM process ontology

3.5. Conclusions

CHAPTER 4. Collaborative methods

4.1. Introduction

4.2. Ontology merging method

4.2.1. Existing ontology merging methods

4.2.2. Ontology merging method

4.2.3. Conclusions

4.3. Sub-ontology extraction method

4.3.1. Definitions

4.3.2. Existing Sub-ontology Extraction Methods

4.3.3. Sub-ontology extraction method

4.3.4. Evaluation

4.3.5. Conclusions

4.4. Rule-based interactive interface

4.4.1. DL query

4.4.2. Drools

4.4.3. The rule-based interactive interface

4.4.4. Users' requests

4.4.5. Query generation

4.4.6. Conclusions

4.5. Algorithm selection/Workflow construction

4.6. Conclusions

CHAPTER 5. Experimental evaluation of the proposed framework

5.1. Introduction

5.2. Methodology for the experiments

5.3. Evaluation of the results for the TS dataset

5.3.1. Construction of the DM ontology for TSC problem

5.3.2. Workflow construction for TSC problem

5.3.3. Evaluation of the experiments on 50 TS datasets

5.4. Evaluation of the results for general classification problem

5.4.1. Evaluation of the experiments on LFW dataset

5.4.2. Evaluation of the experiments on other datasets

5.5. Conclusions

CHAPTER 6. Conclusions

Bibliography

List of Figures

List of Tables

List of abbreviations

Glossary of terms

Appendix 1 TSC algorithms in DM ontologies

Appendix 2 The corresponding entities and results of the 50 TS dataset

Appendix 3 Акты внедрения

Appendix 4 Тексты публикаций