Enhancing DBSCAN algorithm for data mining
Surbhi Sharma, Arvind Kumar Sharma, Dinesh Soni · 2017
Today data mining is widely used by companies with a strong consumer focus like retail, financial, communication and marketing organizations. Here technically data mining is the process of extraction of required information from huge databases. It allows users to analyze data from many different dimensions or angles, categorize it and summarize the relationships identified. The ultimate goal of this paper is to propose a methodology for the improvement in DB-SCAN algorithm to improve clustering accuracy. The proposed improvement is based on back propagation algorithm to calculate Euclidean distance in the dynamic manner. Also this paper shows the obtained results of implemented proposed and existing methods and it compares the results in terms of its execution time and accuracy.