Authors: Randel, Rodrigo
Aloise, Daniel
Mladenović, Nenad 
Hansen, Pierre
Title: On the k-Medoids Model for Semi-supervised Clustering
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 11328 LNCS
First page: 13
Last page: 27
Conference: 6th International Conference on Variable Neighborhood Search, ICVNS 2018; Sithonia; Greece; 4 October 2018 through 7 October 2018
Issue Date: 1-Jan-2019
Rank: M33
ISBN: 978-3-030-15842-2
ISSN: 0302-9743
DOI: 10.1007/978-3-030-15843-9_2
Abstract: 
Clustering is an automated and powerful technique for data analysis. It aims to divide a given set of data points into clusters which are homogeneous and/or well separated. A major challenge with clustering is to define an appropriate clustering criterion that can express a good separation of data into homogeneous groups such that the obtained clustering solution is meaningful and useful to the us...
Keywords: k-medoids | Semi-supervised clustering | Variable Neighborhood Search
Publisher: Springer Link

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