Journal of Information Security Research Alwatben Batoul Rashed A, Hazlina Hamdan, Nurfadhlina Mohd Sharef, Md Nasir Sulaiman, Razali Yaakob, Mansir Abubakar 11 1 2020 https://doi.org/10.6025/jisr/2020/11/1/21-30 http://www.dline.info/jisr/fulltext/v11n1/jisrv11n1_3.pdf Clustering as an unsupervised method is used as a solution technique in various fields to divide and restructure data to become more significant and to transform them into useful information. Currently, clustering is being a difficult problem and complex phenomena since an appropriate number of clusters is unknown, the large number of potential solutions, and the dataset being unsupervised. The problems can be addressed by Multi-objective Particle Swarm Optimization (MOPSO). In Knowledge Discovery settings, complex optimization problems are globally explored with Particle Swarm Optimization (PSO). Lack of appropriate leader selection method becomes a serious issue associated with PSO techniques. In an attempt to address this problem, we proposed a clustering-based method that utilizes the crowding distance (CD) technique to balance the optimality of the objectives in Pareto optimal solution search. We evaluated our method against five clustering approaches that have succeeded in optimization, these are: The K-means Clustering, the IMCPSO, the Spectral clustering, the Birch, and the average-link algorithms. The results of the evaluation show that our approach exemplifies the state-of-the-art methods with significance difference in all most all the tested datasets.