Earticle

초록

영어

Measuring the similarity between nominal variables is an important problem in data mining. It's the base to measure the similarity of data objects which contain nominal variables. There are two kinds of traditional methods for this task, the first one simply distinguish variables by same or not same while the second one measures the similarity based on co-occurrence with variables of other attributes. Though they perform well in some conditions, but are still not enough in accuracy. This paper proposes an algorithm to measure the similarity between nominal variables of the same attribute based on the fact that the similarity between nominal variables depends on the relationship between subsets which hold them in the same dataset. This algorithm use the difference of the distribution which is quantified by f-divergence to form feature vector of nominal variables. The theoretical analysis helps to choose the best metric from four most common used forms of f-divergence. Time complexity of the method is linear with the size of dataset and it makes this method suitable for processing the large-scale data. The experiments which use the derived similarity metrics with K-modes on extensive UCI datasets demonstrate the effectiveness of our proposed method.

목차

Abstract
 1. Introduction
 2. Proposed Algorithm
  2.1 Definition of Similarity
  2.2 Hellinger Distance
  2.3 Distance in Unsupervised Learning
 3. Theoretical Analysis
  3.1 Why Hellinger Distance
  3.2 Complexity of the Algorithm
 4. Experiments
  4.1 Intrinsic Method
  4.2. The Extrinsic Method
 5. Conclusion
 References

키워드

저자

• Zhao Liang [ Institute of Graduate, Liaoning Technical University, Fuxin, Liaoning, 123000, P.R. China ]
• Liu Jianhui [ School of Electronic and Information Engineering, Liaoning Technical University, Huludao, Liaoning, 125000, P.R. China ]

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