Earticle

초록

영어

Regarding high-dimensional heterogeneous data, combined with the existing algorithms' poor mining accuracy and parameter sensitivity, this paper proposes a local outlier mining algorithm based on neighborhood density. Use region segmentation to split high-dimensional data into reasonable sub-regions, reducing the difficulty of processing a large amount of high-dimensional data. The kernel neighborhood density is used to replace the average neighborhood density, so that the density calculation has nothing to do with data heterogeneity. Finally, the neighborhood state and outlier state of the data are further determined on the basis of neighborhood density to improve the accuracy of outlier mining. Through artificial and UCI data set simulation results, it shows that data volume and data dimension are the main factors that affect data outlier mining. The accuracy, coverage, and efficiency of the algorithm proposed in this paper are significantly better than those of the comparison algorithm, and it has better adaptability to different types of data sets.

목차

ABSTRACT
1. Introduction
2. The principle of outlier mining in heterogeneous data
3. Data set region segmentation
4. Outlier mining algorithm
4.1. Outlier detection based on kernel neighborhood density
4.2 Outlier score
5. Simulation and result analysis
5.1. Experimental environment and evaluation indicators
5.2. Artificial data set
5.3 UCI data set
6. Conclusion
References

키워드

저자

• Somenath Chakraborty [ Dept. of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, USA ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  컴퓨터게임및콘텐츠논문지(구 한국컴퓨터게임학회논문지) [Journal of Computer Games and Contents]

• 간기

  월간

• pISSN

  3091-7409

• eISSN

  3092-3638

• 수록기간

  2002~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 691 DDC 793