Data Mining Techniques for Temporal Point Processes Applied to Insurance Claims Data

Data Mining Techniques for Temporal Point Processes Applied to Insurance Claims Data

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We explore data mining on databases consisting of insurance claims information. This dissertation focuses on two major topics we considered by way of data mining procedures. One is the development of a classification rule using kernels and support vector machines. The other is the discovery of association rules using the Apriori algorithm, its extensions, as well as a new association rules technique.For example, suppose that we have a database that only contains the codes C\, C2, and C3 and suppose that absdiff for these codes is 100, 200, and 300 respectively. Let C be the set of codes that are included after the selection process.


Title:Data Mining Techniques for Temporal Point Processes Applied to Insurance Claims Data
Author: Todd Ashley Iverson
Publisher:ProQuest - 2008
ISBN-13:

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