Please use this identifier to cite or link to this item:
https://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 |
Files in This Item:
File | Description | Size | Format | |
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Mostafa EL HABIB DAHO.pdf | 646,2 kB | Adobe PDF | View/Open |
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