Earticle

초록

영어

Most of the existing outlier detection methods aim at numerical data, but there will be a large number of categorical data in real life. Some outlier detection algorithms have been designed for categorical data. There are two main problems of outlier detection for categorical data, which are the similarity measure between categorical data objects and the detection efficiency problem. A cloud model-based outlier detection algorithm for categorical data is proposed in this paper. The algorithm is based on data driven idea and does not require the user to specify parameters. We utilize the synthetic data set and real data set to verify, compare our algorithm with the existing outlier detection algorithms for categorical data, and the experimental result demonstrates that our proposed algorithm has a higher detection rate and lower false alarm rate, while the time complexity is also more competitive.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Principle of Cloud Model
  3.1. Concept and Digital Characteristics of Cloud Model
  3.2. Cloud Generator
 4. Categorical Data Feature Extraction
 5. Cloud Model-based Outlier Detection for Categorical Data
 6. Experiments and Analysis
  6.1. Experimental Setup
  6.2. Analysis for Results
 7. Conclusions
 Acknowledgments
 References

키워드

저자

• Dajiang Lei [ School of Computer Science and Technology, Chongqing University of Posts and Telecommunications ] Corresponding Author
• Liping Zhang [ College of Mobile Telecommunications, Chongqing University of Posts and Telecommunications ]
• Lisheng Zhang [ School of Computer Science and Technology, Chongqing University of Posts and Telecommunications ]

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