Partition Affinity Propagation for Clustering Large Scale of Data in Digital Library

Data clustering is very useful in helping users visit the large scale of data in digit library. In this paper, we present an improved algorithm for clustering large scale of data set with dense relationship based on Affinity Propagation. First, the input data are divided into several groups and Affinity Propagation is applied to them respectively. Results from first step are grouped together in some way, and Affinity Propagation is implemented to them. Experimental results show that our algorithm, referred to as Partition Affinity Propagation, brings an encouraging effect for speeding up Affinity Propagation in clustering dense data set, while clustering accuracy are almost kept or even better. Index Terms — Algorithms, Affinity Propagation, Clustering methods, Dense Data, Experimentation, Performance.