Published Paper Details:

Data mining is the process is to extract information from a data set and transform it into an understandable structure. There are several major data mining techniques have been developing and using in data mining projects recently including classification, clustering, prediction, sequential patterns and decision tree. With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. Data mining refers to the process of retrieving knowledge by discovering novel and relative patterns from large datasets. Analyzing bank databases for analyzing customer behavior is difficult since Bank databases are multi-dimensional, comprised of monthly account records and daily transaction records. Clustering the datasets, assessment and the way of expressing customer's demands and the provinces of requests should be recognized for providing services to the customers, banks, financial and credit institute. It can make a group of abstract objects into classes of similar objects. In the clustering, firstly partition the set of data into groups based on data similarity and then assigns the labels to the groups. The overall goal of this research work is to evaluate the performance of HAC, K-means and density based clustering (DBSCAN) data mining algorithms by considering the different data sets. HAC is a method of cluster analysis which seeks to build a hierarchy of clusters. It has bottom-up and top-down approach. K-means clustering to partition n observations into K clusters in which each observation belongs to the cluster with the nearest mean. Density based clusters are the dense areas in the data space separated from each other by sparse areas. This research presents a comparative analysis for various clustering algorithms. In experiments the effectiveness of algorithms is evaluated by comparing the results on the datasets.