Time series: theory and methods
Time series: theory and methods
Algorithms for clustering data
Algorithms for clustering data
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Generating representative Web workloads for network and server performance evaluation
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Multivariate Data Reduction and Discrimination with SAS Software
Multivariate Data Reduction and Discrimination with SAS Software
Structural analysis of network traffic flows
Proceedings of the joint international conference on Measurement and modeling of computer systems
A pragmatic approach to dealing with high-variability in network measurements
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Assessing the Robustness of Self-Managing Computer Systems under Highly Variable Workloads
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Optimal multi-scale patterns in time series streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Traffic classification using clustering algorithms
Proceedings of the 2006 SIGCOMM workshop on Mining network data
Local Correlation Tracking in Time Series
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Models and framework for supporting runtime decisions in Web-based systems
ACM Transactions on the Web (TWEB)
Advanced Data Mining Techniques
Advanced Data Mining Techniques
| Hi-index | 0.00 |
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.