Main Article Content
Abstract
Human life is surrounded by era of computer generation. In every field whether science and technology, entertainment, profession, research, there is no place where we are not dealing with data and extraction of data which leads to data warehousing and data mining. Customer modeling is an important business application which uses data mining techniques for analysis purpose. Clustering is the popular method of mining where group of similar objects also known as clusters are formed which is highly dissimilar to other clusters. The present work compares the performance of clustering algorithms K-means, Self organized maps and Hierarchical clustering algorithm after applying them to banking dataset. Banking systems use cluster analysis to develop a customer’s topology to retain the loyal customers by designing the best possible financial solutions to specific clusters. Experiment result will show the best accuracy, higher robustness and generalization ability in one of the algorithm.