Earticle

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

Abstract
 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

저자

• Divya Tomar [ Indian Institute of Information Technology, Allahabad ]
• Sonali Agarwal [ Indian Institute of Information Technology, Allahabad ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605