원문정보
초록
영어
Knowledge Discovery in Databases (KDD) covers various processes of exploring useful information from voluminous data. These data may contain several inconsistencies, missing records or irrelevant features, which make the knowledge extraction, a difficult process. So, it is essential to apply pre-processing techniques to these data in order to enhance its quality. Detailed description of data cleaning, imbalanced data handling and dimensionality reduction pre-processing techniques are depicted in this paper. Another important aspect of Knowledge Discovery is to filter, integrate, visualize and evaluate the extracted knowledge. In this paper, several visualization techniques such as scatter plots, parallel co-ordinates and pixel oriented technique are explained. The paper also includes detail descriptions of three visualization tools which are DBMiner, Spotfire and WinViz along with their comparative evaluation on the basis of certain criteria. It also highlights the research opportunities and challenges of Knowledge Discovery process.
목차
1. Introduction
2. Pre-Processing Techniques
2.1. Data Cleaning
2.2. Handling Imbalanced Dataset
2.3. Data Transformation
2.4. Dimensionality Reduction
3. Post Processing Techniques
3.1. Knowledge Filtering
3.2. Evaluation
3.3. Information Visualizatione
3.4. Knowledge Integration
4. Research Opportunities and Challenges of KDD
5. Conclusion
References
키워드
저자정보
참고문헌
- 1(Reference title not available)
- 2Parallel coordinates: a tool for visualizing multi-dimensional geometry네이버 원문 이동
- 3Projection Pursuit Regression네이버 원문 이동
- 4Graphical Methods for Data Analysis네이버 원문 이동
- 5(Reference title not available)
- 6VisDB: database exploration using multidimensional visualization네이버 원문 이동
- 7(Reference title not available)
- 8The feature selection problem: traditional methods and a new algorithm네이버 원문 이동
- 9Principal Component Analysis네이버 원문 이동
- 10(Reference title not available)