Implementation Of Data Mining Clustering Astra Vehicle Insurance Customers With The Clustering Method

Abstract

Data mining, which is referred to as knowledge discovery in databases (KDD), means the nontrival process of extracting implicit, previously unknown and useful information such as knowledge rules, descriptions, regularities, and major trends from large data bases. Data mining is developing in a multidisciplinary field, including database technology, machine learning, artificial intelligence, neural networks, information retrieval, and so on. K-Means is a non-hierarchical clustering method that tries to partition existing data into one or more clusters/groups. So that data that has the same characteristics and data that has different characteristics are grouped into other groups. The purpose of this research is to find out what types of insurance groups are most interested in based on their respective addresses, especially the city of Medan. The method used is group analysis with the K-Means method. As for the results of grouping the two variables, namely the type of insurance that is in great demand for the first place is the Planned Scholarship (D6) with a total of 10 and the second order is Dwiguna Prima (DP) with a total of 40 and for the third order is Lifetime Prima (WP) with a total of 50.