Earticle

초록

영어

Outlier detection and clustering technologies are an important branch of data mining, such as combining the two technologies can improve the mining significance. In this paper, both clustering and outlier detection can be the starting point, proposed a DBSCAN-LOF algorithm is the core idea is to use k_ neighbors thought, DBSCAN redefine the core of the object, making the only non-core objects LOF The operation, thereby reducing the original LOF algorithm is computing the number of global objects, and makes no DBSCAN algorithm input parameters Eps. Real and simulated data sets by experimental results confirm that the algorithm to improve the operating efficiency and the LOF algorithm DBSCAN clustering effect, and while producing clustering and outlier detection results.

목차

Abstract
 1. Introduction
 2. Clustering Outlier Detection Algorithm DBSCAN-LOF
  2.1. The Description of DBSCAN-LOF
 3. Experiment Analysis
 4. Conclusion
 References

키워드

저자

• Huangtao [ Harbin University of science and Technology, Harbin, China ]
• Tan Yanna [ China United Network Communications Corp Harbin branch, Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605