Paper Info
Title
Adapted B-CUBED Metrics to Unbalanced Datasets
Abstract
B-CUBED metrics have recently been adopted in the evaluation of clustering results as well as in many other related tasks. However, this family of metrics is not well adapted when datasets are unbalanced. This issue is extremely frequent in Web results, where classes are distributed following a strong unbalanced pattern. In this paper, we present a modified version of B-CUBED metrics to overcome this situation. Results in toy and real datasets indicate that the proposed adaptation correctly considers the particularities of unbalanced cases.
Year
DOI
Venue
2015
10.1145/2766462.2767836
International Conference on Research an Development in Information Retrieval
Keywords
Field
DocType
Evaluation,Search results clustering,Unbalanced datasets
Data mining,Computer science,Artificial intelligence,Cluster analysis,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
7
Authors
2
Name
Order
Citations
PageRank
Jose G. Moreno15010.67
Gaël Dias235441.95