Earticle

초록

영어

Data mining retrieves knowledge information from larger amounts of data. Clustering is an assemble of similar objects in to one class and dissimilar objects in to another class. When designing clustering ensemble on large dimensional data space, both time and space requirements for processing may be overinflated. This tends to impose feature selection methods to remove redundant features and handle the noise data. There are filter, wrapper and hybrid methods in feature selection. This paper shows a tour on types of feature selection techniques and numbers of experiments are conducted to compare feature selection techniques using different datasets with R tool, which gives better technique for clustering ensemble design.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Feature-Selection Methods
  3.1. Filter Methods
  3.2. Wrapper Methods
 4. Results and Discussions
  4.1. Tool Description
  4.2. Data Sets
  4.3. Feature Selection Using Filter Methods
  4.4. Feature Selection Using Wrapper Methods
 5. Conclusion
 References

키워드

저자

• Y. Leela Sandhya Rani [ Department of Computer Science and Engineering, KL University, Vaddeswaram, AP, 522502, India ]
• V. Sucharita [ Department of Computer Science and Engineering, KL University, Vaddeswaram, AP, 522502, India ]
• Debnath Bhattacharyya [ Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Duvvada, Visakhapatnam, India ]
• Hye-Jin Kim [ Sungshin W. University ] Corresponding author

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