Heterogeneous Graph Based Similarity Measure for Categorical Data Unsupervised Learning

Heterogeneous Graph Based Similarity Measure for Categorical Data Unsupervised Learning

Different from numerical attributes, measuring the similarity between categorical attributes is more complex due to their non-inherently ordered characteristic, especially in an unsupervised scheme. This work, therefore, presents a new method, Heterogeneous Graph-based Similarity measure (HGS), to m...

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Bibliographic Details
Main Authors: Yanqing Ye, Jiang Jiang, Bingfeng Ge, Kewei Yang, H. Eugene Stanley
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Unsupervised learning
similarity measure
categorical data
heterogeneous graph-based similarity (HGS)
Online Access:https://ieeexplore.ieee.org/document/8795560/

Internet

https://ieeexplore.ieee.org/document/8795560/

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