Clustering with Quantitative User Preferences on Attributes

Moussawi, Adnan El; Cheriat, Ahmed; Giacometti, Arnaud; Labroche, Nicolas; Soulet, Arnaud

doi:10.1109/ictai.2016.0065

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…A CL constraint implies that two data instances must not be in the same cluster. Other means for expressing constraints are attribute-level constraints [El Moussawi et al 2016], cluster-level constraints [Dubey et al 2010], relative constraints [Liu et al 2011], and labels [Castellano et al 2013].…”

Section: Semi-supervised Clusteringmentioning

confidence: 99%

A Framework for Online Clustering Based on Evolving Semi-Supervision

Alves

Barioni

Faria

2017

Anais Do XXXII Simpósio Brasileiro De Banco De Dados (SBBD 2017)

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The huge amount of currently available data puts considerable constraints on the task of information retrieval. Automatic methods to organize data, such as clustering, can be used to help with this task allowing timely access. Semi-supervised clustering approaches employ some additional information to guide the clustering performed based on data attributes to a more suitable data partition. However, this extra information may change over time imposing a shift in the manner by which data is organized. In order to help cope with this issue, we propose the framework called CABESS (Cluster Adaptation Based on Evolving Semi-Supervision), for online clustering. This framework is able to deal with evolving semi-supervision obtained through user binary feedbacks. To validate our approach, the experiments were run over hierarchical labeled data considering clustering splits over time. The experimental results show the potential of the proposed framework for dealing with evolving semi-supervision. Moreover, they also show that our framework is faster than traditional semi-supervised clustering algorithms using lower standard semi-supervision.

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