Performance Evaluation of Shuffled Frog Leaping Algorithm (SFLA) Clustering Using Real-World Datasets

MATLAB 4K 261 views 0 downloads 1 credits
Tags:

Resource Overview

Evaluation of SFLA clustering performance through real-world datasets with graphical illustrations demonstrating algorithm effectiveness, including implementation insights and key parameter configurations.

Detailed Documentation

By conducting clustering performance evaluation of the Shuffled Frog Leaping Algorithm (SFLA) using real-world datasets, we obtained graphical results that demonstrate the algorithm's performance characteristics. The implementation involves initializing frog populations representing potential cluster centers, applying memetic evolution through local search within subgroups, and global information exchange between subgroups. Key functions include fitness calculation using cluster validity indices like Davies-Bouldin Index, position updating through Lévy flights for local exploration, and crossover operations for global information sharing. The algorithm's convergence behavior and clustering accuracy are visually represented through dendrograms and cluster distribution plots.

You May Also Like