Publication:
An automatic clustering for interval data using the genetic algorithm
An automatic clustering for interval data using the genetic algorithm
No Thumbnail Available
Files
Date
2020
Authors
Tai Vovan
Dinh Phamtoan
Le Hoang Tuan
Thao Nguyentrang
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Abstract
This paper proposes an Automatic Clustering algorithm for Interval data using the Genetic
algorithm (ACIG). In this algorithm, the overlapped distance between intervals is applied to
determining the suitable number of clusters. Moreover, to optimize in clustering, we modify
the Davies & Bouldin index, and to improve the crossover, mutation, and selection operators
of the original genetic algorithm. The convergence of ACIG is theoretically proved and illustrated
by the numerical examples. ACIG can be implemented effectively by the established
Matlab procedure. Through the experiments on data sets with different characteristics, the
proposed algorithm has shown the outstanding advantages in comparison to the existing ones.
Recognizing the images by the proposed algorithm gives the potential in real applications of
this research.
Description
Keywords
Cluster analysis,
DB index,
Genetic algorithm,
Interval data,
Overlap distance