A Comparative Study on Various Clustering Techniques in Data Mining - Kasthuri, M; Kanchana, S; Hemalatha, R (International Journal of Computer Sciences and Engineering, 2025)
Clustering is the process of combination of identical objects into same classes. A cluster is a grouping of data objects that are analogous to one another within the same cluster and are disparate to the objects in other clusters. Data clustering can be performed on various areas such as data mining, statistics, machine learning, spatial database, biology and marketing. Machine learning is classified into supervised and unsupervised learning. Clustering is the example of unsupervised learning that has no predefined classes and deals with unknown samples. Cluster analysis can be done with different types of methods includes partitioning methods, hierarchical methods, density based methods, grid based methods and model based methods. Quality of clusters can be determined by the two factors that they are high intra-cluster similarity and low intercluster similarity. In this paper, various clustering techniques has been analyzed in data mining in terms of methodology adopted, dataset handled, accuracy, advantages and limitations