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DC Field | Value | Language |
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dc.contributor.author | EL HABIB DAHO, Mostafa | - |
dc.contributor.author | SETTOUTI, Nesma | - |
dc.contributor.author | LAKHDARI, Salsabi | - |
dc.contributor.author | SAIDI, Amaria | - |
dc.contributor.author | BECHAR, Mohammed El Amine | - |
dc.contributor.author | SAIDI, Meryem | - |
dc.date.accessioned | 2019-06-10T09:58:46Z | - |
dc.date.available | 2019-06-10T09:58:46Z | - |
dc.date.issued | 2019-03-04 | - |
dc.identifier.uri | http://dspace.univ-ouargla.dz/jspui/handle/123456789/20823 | - |
dc.description | Le 2eme Conference Internationale sur intelligence Artificielle et les Technologies Information ICAIIT 2019 | en_US |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Université Kasdi Merbah Ouargla | - |
dc.subject | Semi-Supervised Clustering | en_US |
dc.subject | learning constraint | en_US |
dc.subject | pairwise constraint | en_US |
dc.subject | COP K-means | en_US |
dc.subject | Kernel Mean Shift Cluster- ing | en_US |
dc.subject | Kernel clustering with Relative distance | en_US |
dc.title | A comparative study of semi-supervised clustering methods with pairwise constraint | en_US |
dc.type | Article | en_US |
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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