Application of Association Rule Learning in Customer Relationship Management




The main purpose of this study is the application of association rule learning using data mining techniques in customer relationship management of a diagnostics centres. Clustering customers is needed to find unsatisfied need, promote services packages and create new service packages. The proposed system diagnostics data mining system (DDMS) consists of three components; pre-processing, clustering and post processing. The data collected is for a period of four month for 6700 transaction. Three data sets are constructed from the original data set by dividing the whole data into 90%, 85% and 80% for training and 10%, 15% and 20% for testing respectively. Three K-means model are used with k=10, 15 and 18 cluster and each data set is used to calibrate and test the model for a total of nine ones. It is found that the best model is the one with 15 clusters. The clustering results are represented to a health and diagnostics personnel who found that some results are reasonable and others go along with the policy guiding customer relationship management in the centers

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