Computer Science ›› 2016, Vol. 43 ›› Issue (6): 280-282.doi: 10.11896/j.issn.1002-137X.2016.06.055

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Hellinger Distance Based Similarity Analysis for Categorical Variables in Mixture Dataset

ZHAO Liang, LIU Jian-Hui and WANG Xing   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Similarity analysis of categorical variables is an important part of data mining.The traditional methods have the defects of neglecting the difference between categorical variables,which are seriously affected by unbalanced dataset and can not be used in mixture dataset.To overcome the shortcomings mentioned above,this paper proposed an algorithm to measure the similarity between categorical variables based on the Hellinger distance.It accumulates the distribution differences of variables with different attributes in subsets corresponding to categorical variables as similarity variables and fits for mixture dataset.The experiments which use the derived similarity metrics in clustering algorithm and apply UCI datasets show that there is significant improvement in accuracy,validity and stability.

Key words: Categorical variables,Similarity,f-divergence,Hellinger distance

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