Open AccessSimilarity metrics for classification: A Review
Author(s) -
Shaker K. Ali,
Zahoor M. Aydam,
Biadaa M. Rashed
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/928/3/032052
Subject(s) - cluster analysis , similarity (geometry) , metric (unit) , computer science , data mining , artificial intelligence , disadvantage , machine learning , pattern recognition (psychology) , image (mathematics) , engineering , operations management
In this paper, fourteen similarity metrics are reviews, which will be the most important part in Diagnosis, Classification, Clustering and Recognition. Most researchers may not sure to choose which metric will be powerful and give high accuracy in them researches. Therefore, this paper will be as a guide for them to select which metric useful for them research by try one of fourteen metrics that listed in this paper and can compare one of these metrics by advantage and disadvantage of each one. In addition, there are new metrics modified to give more accuracy by testing them in some clustering application.