A bio-inspired hierarchical clustering algorithm with backtracking strategy

Biological entities, such as birds with their flocking behavior, ants with their social colonies, fish with their shoaling behavior and honey bees with their complex nest construction, represent a great source of inspiration in the optimization and data mining domains. Following this line of thought...

Full description

Saved in:
Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2015-03, Vol.42 (2), p.174-194
Main Authors: Elkamel, Akil, Gzara, Mariem, Ben-Abdallah, Hanêne
Format: Article
Language:English
Subjects:
Adaptation
Agglomeration
Algorithms
Ant colony optimization
Artificial Intelligence
Behavior
Birds
Cluster analysis
Clustering
Computer Science
Data mining
Datasets
Decisions
Image retrieval
Intelligence
Machines
Manufacturing
Mechanical Engineering
Optimization
Processes
Strategy
Traveling salesman problem
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:Biological entities, such as birds with their flocking behavior, ants with their social colonies, fish with their shoaling behavior and honey bees with their complex nest construction, represent a great source of inspiration in the optimization and data mining domains. Following this line of thought, we propose the Communicating Ants for Clustering with Backtracking strategy (CACB) algorithm, which is based on a dynamic and an adaptive aggregation threshold and a backtracking strategy where artificial ants are allowed to turn back in their previous aggregation decisions. The CACB algorithm is a hierarchical clustering algorithm that generates compact dendrograms since it allows the aggregation of more than two clusters at a time. Its high performance is experimentally shown through several real benchmark data sets and a content-based image retrieval system.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-014-0573-6