An Improved Pheromone-Based Kohonen Self- Organising Map in Clustering and Visualising Balanced and Imbalanced Datasets (Journal of Information and Communication Technology, 2021)
The data distribution issue remains an unsolved clustering problem in data mining, especially in dealing with imbalanced datasets. TheKohonen Self-Organising Map (KSOM) is one of the well-known clustering algorithms that can solve various problems without a pre-defined number of clusters. However, similar to other clustering algorithms, this algorithm requires sufficient data for its unsupervisedlearning process. The inadequate amount of class label data in a dataset significantly affects the clustering learning process, leadingto inefficient and unreliable results. Numerous research have been conducted by hybridising and optimising the KSOM algorithmwith various optimisation techniques. Unfortunately, the problems are still unsolved, especially separation boundary and overlappingclusters. Therefore, this research proposed an improved pheromonebased PKSOM algorithm known as iPKSOM to solve the mentionedproblem. Six different datasets, i.e. Iris, Seed, Glass, Titanic, WDBC, and Tropical Wood datasets were chosen to investigate the effectiveness of the iPKSOM algorithm. All datasets were observed and compared with the original KSOM results. This modificationsignificantly impacted the clustering process by improving and refining the scatteredness of clustering data and reducing overlappingclusters. Therefore, this proposed algorithm can be implemented in clustering other complex datasets, such as high dimensional andstreaming data.