Abstract:

To enhance service efficiency on cluster resource, administrator needs to make classification of users, and provide various strategies on resource utilization to different users. DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm can achieve users classification, but the initial parameters are very sensitive. The improved algorithm classifies the level of density firstly, then gets the densitythreshold of each level, and uses DBSCAN under each threshold which solves the problem of global  parameters. It uses a sorted queue of directly accessible distance as an innovation, makes the sorting information as variable parameter to decrease the influence of initial parameter. The algorithm has verified its feasibility through example data of HPC users. The experimental result demonstrates that this improved algorithm can achieve a more accurate and comprehensive user classification.

Key words: clustering analysis, density based spatial clustering of applications with noise (DBSCAN), high-performance computing center, users classification, data mining