IMPLEMENTATION AND TESTING AFFINITY PROPAGATION AND ADAPTIVE AFFINITY PROPAGATION ALGORITHMS IN STUDENT DATA BASED ON GPA AND HOME DISTANCE

Clustering is method to classify data easily that has purpose is to look at the correlation between data attributes. Clustering is grouping data points process based on similarity value to determine cluster center. Affinity Propagation (AP) and Adaptive Affinity Propagation (Adaptive AP) are clustering algorithms that produce number of cluster, cluster members and exemplar of each cluster. To find out more effective algorithm when clustering data, there is needs to implements and tests both algorithms. This clustering application was implemented those algorithms by Matlab R2013a 8.10 using Gunadarma University student data based on GPA and home distance as much as 250 records. The result of testing this clustering application was analyzed by comparing both algorithms to find out one of the best algorithm in this case and to find out the correlation between home distance and student GPA in Gunadarma University.