Integrating distance metric learning and cluster-level constraints in semi-supervised clustering

Semi-supervised clustering has been widely explored in the last years. In this paper, we present HCAC-ML (Hierarchical Confidence-based Active Clustering with Metric Learning), an innovative approach for this task which employs distance metric learning through cluster-level constraints. HCAC-ML is b...

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Published in2017 International Joint Conference on Neural Networks (IJCNN) pp. 4118 - 4125
Main Authors Magalhaes Nogueira, Bruno, Benevides Tomas, Yuri Karan, Marcondes Marcacini, Ricardo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2017
Subjects
Clustering algorithms
Context
Covariance matrices
Electronic mail
Measurement
Partitioning algorithms
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Summary:Semi-supervised clustering has been widely explored in the last years. In this paper, we present HCAC-ML (Hierarchical Confidence-based Active Clustering with Metric Learning), an innovative approach for this task which employs distance metric learning through cluster-level constraints. HCAC-ML is based on the HCAC algorithm, an state-of-the-art algorithm for hierarchical semi-supervised clustering that uses an active learning approach for inserting cluster-level constraints. These constraints are presented to a variation of ITML (Information-theoretic Metric Learning) algorithm to learn a Mahalanobis-like distance function. We compared HCAC-ML with other semi-supervised clustering algorithms in 26 different datasets. Results indicate that HCAC-ML outperforms other algorithms in most of the scenarios, but specially when the number of constraints is small. This makes HCAC-ML useful in practical applications.
ISSN:2161-4407
DOI:10.1109/IJCNN.2017.7966376