Parametric sensitivity analysis of cOptBees optimal clustering algorithm

Clustering is one of the most important tasks in data mining and can be defined as the process of partitioning objects into groups or clusters, such that objects in the same group are more similar to one another than to objects belonging to other groups. Many algorithms to solve data clustering prob...

Full description

Saved in:
Bibliographic Details
Main Authors: Ferreira Cruz, Davila Patricia, Dourado Maia, Renato, Nunes de Castro, Leandro
Format: Conference Proceeding
Language:English
Subjects:
bee-inspired algorithms
dynamic size population
optimal data clustering
swarm intelligence
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Clustering is one of the most important tasks in data mining and can be defined as the process of partitioning objects into groups or clusters, such that objects in the same group are more similar to one another than to objects belonging to other groups. Many algorithms to solve data clustering problems have been presented in the literature. Recently, bee-inspired clustering algorithms have been proposed, presenting good performance to find groups in data. This paper aims to present the parametric sensitivity analysis of cOptBees, a bee-inspired clustering algorithm designed to find optimal clusters in datasets. The algorithm was run for different parameter configurations to assess the influence of each parameter in its performance.
ISSN:2164-7143
2164-7151
DOI:10.1109/ISDA.2014.7066266