Modification fitness function of particle swarm optimization to improve the cluster centroid
Chiabwoot Ratanavilisagul · IOP Conference Series Materials Science and Engineering · 2020
Abstract Clustering is a classification technique. The clustering is brought to create models. The model is applied with the new input data to identify the class of that data. The goal of clustering is finding the best cluster centroid. The process of searching the best cluster centroid can use Particle Swarm Optimization. Many researches use it and obtain good results. But, searching the best cluster centroid by PSO suffers the problem from calculating the fitness function, because the fitness function is calculated by distances from the cluster centroid to all data inputs. These fitness functions may lead to the bad cluster centroid. So, this paper proposed the fitness function is calculated by the results from classification. The results from classification can lead to the good cluster centroid and create a good model to apply with the new input data. The proposed technique is tested on seven datasets from the UCI Machine Learning Repository and gives more satisfied search results in comparison with PSOs for the data clustering problems.