APPLICATION OF ASSOCIATION RULE LEARNING IN CUSTOMER RELATIONSHIP MANAGEMENT. A RESEARCH PROJECT MATERIAL ON COMPUTER SCIENCE EDUCATION
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.
1.1 DATA MINING
Data mining is the process that uses a variety of data analysis and modelling techniques to discover patterns and relationships in data that may be used to make accurate predictions (Guarav andAggraval, 2012).
It’s described as the process of extracting knowledge data discovery of valid, authentic and actionable information from large data bases. It is also used to derive patterns and trends that exist in the collected data ( Masheswari et al, 2014).
Data mining is a continuous iterative process that is the very core of business intelligence. It involves the use of data mining software, sound methodology and human creativity to achieve new insight through the exploration of data to uncover patterns, relationships, anomalies and dependencies (PuneetShukla, 2015).According to (PuneetShukla, 2015) the process of data mining consists of three stages which are the Initial exploration, Model building or pattern identification with validation/verification, Deployment (i.e. the application of the model to new data in order to generate predictions).
Data mining consists of five major elements which includes extracting, transform and load data onto data warehouse systems, Storing and manage data , provide data access to business analysts and information technology professionals, analyse the data by application software and present the datain a useful format such as a graph or table.
Data mining involves six common classes of tasks which are;