Please use this identifier to cite or link to this item: http://dspace.univ-ouargla.dz/jspui/handle/123456789/20823
Title: A comparative study of semi-supervised clustering methods with pairwise constraint
Authors: EL HABIB DAHO, Mostafa
SETTOUTI, Nesma
LAKHDARI, Salsabi
SAIDI, Amaria
BECHAR, Mohammed El Amine
SAIDI, Meryem
Keywords: Semi-Supervised Clustering
learning constraint
pairwise constraint
COP K-means
Kernel Mean Shift Cluster- ing
Kernel clustering with Relative distance
Issue Date: 4-Mar-2019
Publisher: Université Kasdi Merbah Ouargla
Abstract: Abstract—Semi-Supervised Clustering (SSC) is a largely unsu- pervised learning task that seeks to guide the clustering process through constraint, and combines several methods with different approaches. In this work, our interest is more focused on the semi-supervised clustering with constraint approaches, and more particularly those based on the pairwise constraint. This paper establishes a comparative study between 3 algorithms mainly: the Constrained K-means algorithm which applies constraint of comparison between pairs of objects, called COP-KMEANS, the Semi-supervised kernel clustering with relative distance Algorithm (SKLR) and the Semi-supervised Kernel Mean Shift clustering Algorithm (SKMS). Experimental results indicate that the semi-supervised kernel Mean Shift clustering method can generally outperform the other semi-supervised methods. The experimental study shows that the use of constraint can improve performance especially when the number of available labeled examples is insufficient to build a decision model.
Description: Le 2eme Conference Internationale sur intelligence Artificielle et les Technologies Information ICAIIT 2019
URI: http://dspace.univ-ouargla.dz/jspui/handle/123456789/20823
Appears in Collections:2. Faculté des nouvelles technologies de l’information et de la communication

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