Improving categorical data clustering algorithm by weighting uncommon attribute value matches | Zendy

Zengyou He | Zendy; Xiaofei Xu | Zendy; Shenchun Deng | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Improving categorical data clustering algorithm by weighting uncommon attribute value matches

Author(s) -

Zengyou He,

Xiaofei Xu,

Shenchun Deng

Publication year - 2006

Publication title -

computer science and information systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.244

H-Index - 24

eISSN - 2406-1018

pISSN - 1820-0214

DOI - 10.2298/csis0601023h

Subject(s) - categorical variable , cluster analysis , computer science , weighting , computation , data mining , similarity (geometry) , cure data clustering algorithm , value (mathematics) , data stream clustering , algorithm , pattern recognition (psychology) , correlation clustering , artificial intelligence , machine learning , image (mathematics) , medicine , radiology

This paper presents an improved Squeezer algorithm for categorical data clustering by giving greater weight to uncommon attribute value matches in similarity computations. Experimental results on real life datasets show that, the modified algorithm is superior to the original Squeezer algorithm and other clustering algorithm with respect to clustering accuracy.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research