Real-world data collections are often heterogeneous (represented by a set of mixed attributes data types: numerical, categorical and fuzzy); since most available similarity measures can only be applied to one type of data, it becomes essential to construct an appropriate similarity measure for comparing such complex. In this paper, a framework of new and… (More)

@inproceedings{Bashon2012SoftCA,
title={Soft Computing A Framework for Comparing Heterogeneous Objects: on the Similarity Measurements for Fuzzy, Numerical and Categorical Attributes},
author={Yasmina Bashon and Daniel Neagu and Mick J. Ridley},
year={2012}
}