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Multi-label classification via incremental clustering on an evolving data stream.

Nguyen, Tien Thanh; Dang, Manh Truong; Luong, Anh Vu; Liew, Alan Wee-Chung; Liang, Tiancai; McCall, John


Manh Truong Dang

Anh Vu Luong

Alan Wee-Chung Liew

Tiancai Liang


With the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make predictions, contributing to the competitive advantages of companies. The increasing data volume, however, has posed many problems to classical batch learning systems, such as the need to retrain the model completely with the newly arrived samples or the impracticality of storing and accessing a large volume of data. This has prompted interest on incremental learning that operates on data streams. In this study, we develop an incremental online multi-label classification (OMLC) method based on a weighted clustering model. The model is made to adapt to the change of data via the decay mechanism in which each sample's weight dwindles away over time. The clustering model therefore always focuses more on newly arrived samples. In the classification process, only clusters whose weights are greater than a threshold (called mature clusters) are employed to assign labels for the samples. In our method, not only is the clustering model incrementally maintained with the revealed ground truth labels of the arrived samples, the number of predicted labels in a sample are also adjusted based on the Hoeffding inequality and the label cardinality. The experimental results show that our method is competitive compared to several well-known benchmark algorithms on six performance measures in both the stationary and the concept drift settings.


NGUYEN, T.T., DANG, M.T., LUONG, A.V., LIEW, A. W.-C., LIANG, T. and MCCALL, J. 2019. Multi-label classification via incremental clustering on an evolving data stream. Pattern recognition [online], 95, pages 96-113. Available from:

Journal Article Type Article
Acceptance Date Jun 1, 2019
Online Publication Date Jun 3, 2019
Publication Date Nov 30, 2019
Deposit Date Jul 15, 2019
Publicly Available Date Jun 4, 2020
Journal PatternrRecognition
Print ISSN 0031-3203
Electronic ISSN 1873-5142
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 95
Pages 96-113
Keywords Multi-label classification; Incremental learning; Online learning; Clustering; Data stream; Concept drift
Public URL


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