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2005 Technical Reports

Multi-Layer Increasing Coherence Clustering of Autonomous Systems with MULICsoft

Bill Andreopoulos, Aijun An and Xiaogang Wang

Technical Report CS-2005-07

York University

April 2005


Discovering the properties of the Internet topology is crucial for the use, maintenance and optimization of the Internet. A recent approach in the research community is to collect IP path data from active probing of hosts and to map the IP paths to known Autonomous System (AS) paths. We apply the MULICsoft clustering algorithm to network data for ASs forming the Internet backbones. MULICsoft is intended for categorical data sets where each categorical attribute value (CA) has a 'weight' in the range 0.0 to 1.0, indicating how strongly the corresponding CA should influence the clustering process. MULICsoft produces as many clusters as it can find in the data set. Each cluster consists of layers formed gradually through iterations, by reducing the similarity criterion for inserting objects in layers of a cluster at different iterations. The objects in the data are ASs and the CAs represent links between ASs. The weights are inversely related to the number of unknown IPs in an indirect link between ASs. We introduce a novel significance metric for identifying the most significant ASs in a cluster.

Notice:The work presented in the paper above is covered by pending patents and copyright. Publication of this paper does not grant rights to any intellectual property. All rights reserved.

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