Fast semi-supervised evidential clustering

Semi-supervised clustering is a constrained clustering technique that organizes a collection of unlabeled data into homogeneous subgroups with the help of domain knowledge expressed as constraints. These methods are, most of the time, variants of the popular k-means clustering algorithm. As such, th...

Full description

Saved in:
Bibliographic Details
Published in:International journal of approximate reasoning 2021-06, Vol.133, p.116-132
Main Authors: Antoine, Violaine, Guerrero, Jose A., Xie, Jiarui
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Constrained clustering
Evidential clustering
Label constraints
Optimization
Theory of belief functions
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Semi-supervised clustering is a constrained clustering technique that organizes a collection of unlabeled data into homogeneous subgroups with the help of domain knowledge expressed as constraints. These methods are, most of the time, variants of the popular k-means clustering algorithm. As such, they are based on a criterion to minimize. Amongst existing semi-supervised clusterings, Semi-supervised Evidential Clustering (SECM) deals with the problem of uncertain/imprecise labels and creates a credal partition. In this work, a new heuristic algorithm, called SECM-h, is presented. The proposed algorithm relaxes the constraints of SECM in such a way that the optimization problem is solved using the Lagrangian method. Experimental results show that the proposed algorithm largely improves execution time while accuracy is maintained.
ISSN:0888-613X
1873-4731
DOI:10.1016/j.ijar.2021.03.008