An Interactive System for Association Rule Discovery for Life Assurance
Rafea, Ahmed A.
Shafik, Suzanne S.
Shaalan, Khaled F.
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This paper uses prior domain knowledge to guide the mining of association rules in life assurance business environment. This approach is used in order to overcome the drawbacks of data mining using rule induction such as loss of information, discover too many obvious patterns, and mining of overwhelmed association rules. A data mining interactive rule induction algorithm is introduced to mine rules at micro levels. The mined rules describe the impact of different insurance policies attributes, customer profiles, and market channels on company portfolio growth. A system was built based on this algorithm and was tested and verified on real data set in Misr Insurance company, which is the leading insurance company in Egypt.