Data clustering based on correlation analysis applied to highly variable domains

Clustering of traffic data based on correlation analysis is an important element of several network management objectives including traffic shaping and quality of service control. Existing correlation-based clustering algorithms are affected by poor results when applied to highly variable time series characterizing most network traffic data. This paper proposes a new similarity measure for computing clusters of highly variable data on the basis of their correlation. Experimental evaluations on several synthetic and real datasets show the accuracy and robustness of the proposed solution that improves existing clustering methods based on statistical correlations.