Friday 24th of November 2017
 

Using Data Mining for Life Insurance Network Optimization


Brunela Karamani, Esteriana Haskasa and Shkelqim Kuka

Managing the large amounts of information and efficiently using this information in improved decision making has become increasingly challenging. In this paper we will demonstrate how genetic algorithms integrated in MATLAB help in optimizing a life insurance network. We will analyze the case of study of a life insurance company which wants to enlarge its network. We will explain how we have calculated the fitness function and how we have implemented the data mining optimizing technique of cluster analysis. We will use MATLAB to deliver the results of our study, accompanied them with appropriate explanations. We will conclude arguing the benefits of our study.

Keywords: optimizing, network, data mining, cluster analysis, genetic algorithm, fitness function, insurance

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ABOUT THE AUTHORS

Brunela Karamani
Brunela Karamani is a pedagogue in Polytechnic University, in Computer Engineering Department. She has 12 years teaching experience in computer science. In 2010 she has finished the Master Thesis and now is PhD student. Her research areas of interest are Data mining and Statistical solution.

Esteriana Haskasa
Esteriana Haskasa is a pedagogue at the University of Elbasan “Aleksander Xhuvani”. She has 4 years of programming experience, and 2 years of teaching experience. Her research areas of interest are Artificial Intelligence and Robotics.

Shkelqim Kuka
Shkelqim Kuka is a Ass.Professor in Polytechnic University, in Department of Mathematics. He has 25 years teaching experience in algebra and applied mathematics. His research areas of interest are applied mathematics and machine learning.


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