Abstract
Data clustering has always been an important aspect of data mining. Extracting clusters from data could be very difficult especially when the features are large and the classes not clearly partitioned, hence the need for high-quality clustering techniques. The major shortcoming of various clustering techniques is that the number of clusters must be stated before the clustering starts. A recent successful work in clustering is the Clustering analysis based on Glowworm Swarm Optimization (CGSO) algorithm. CGSO uses the multimodal search capacity of the Glowworm Swarm Optimization (GSO) algorithm to automatically figure out clusters within a data set without prior knowledge about the number of clusters. However, the sensor range - one of the parameters of the CGS algorithm and a determinant of the number of clusters as well as the cluster quality - is in fact obtained by trial and error, which is clearly an inefficient approach.
ISIMETO, R & YINKA-BANJO, C (2021). An Enhanced Clustering Analysis Based On Glowworm Swarm Optimization. Afribary. Retrieved from https://tracking.afribary.com/works/an-enhanced-clustering-analysis-based-on-glowworm-swarm-optimization
ISIMETO, ROSELYN and CHIKA YINKA-BANJO "An Enhanced Clustering Analysis Based On Glowworm Swarm Optimization" Afribary. Afribary, 07 May. 2021, https://tracking.afribary.com/works/an-enhanced-clustering-analysis-based-on-glowworm-swarm-optimization. Accessed 24 Nov. 2024.
ISIMETO, ROSELYN, CHIKA YINKA-BANJO . "An Enhanced Clustering Analysis Based On Glowworm Swarm Optimization". Afribary, Afribary, 07 May. 2021. Web. 24 Nov. 2024. < https://tracking.afribary.com/works/an-enhanced-clustering-analysis-based-on-glowworm-swarm-optimization >.
ISIMETO, ROSELYN and YINKA-BANJO, CHIKA . "An Enhanced Clustering Analysis Based On Glowworm Swarm Optimization" Afribary (2021). Accessed November 24, 2024. https://tracking.afribary.com/works/an-enhanced-clustering-analysis-based-on-glowworm-swarm-optimization