Metodos de Agrupamentos em dois Estagios

TL;DR

This paper explores four two-stage clustering methods combining neural networks and optimization algorithms, demonstrating that SOINAK outperforms the others in pattern recognition tasks.

Contribution

Introduces four novel two-stage clustering techniques combining neural networks and ant-based algorithms, with SOINAK showing superior performance.

Findings

SOINAK achieved the best results among the four methods.

The proposed methods are effective for pattern recognition.

SOINN combined with AK improves clustering accuracy.

Abstract

This work investigates the use of two-stage clustering methods. Four techniques were proposed: SOMK, SOMAK, ASCAK and SOINAK. SOMK is composed of a SOM (Self-Organizing Maps) followed by the K-means algorithm, SOMAK is a combination of SOM followed by the Ant K-means (AK) algorithm, ASCAK is composed by the ASCA (Ant System-based Clustering Algorithm) and AK algorithms, SOINAK is composed by the Self-Organizing Incremental Neural Network (SOINN) and AK. SOINAK presented a better performance among the four proposed techniques when applied to pattern recognition problems.