OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Finding community structure in mega-scale social networks: [extended abstract]
Proceedings of the 16th international conference on World Wide Web
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
DENGRAPH: A Density-based Community Detection Algorithm
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
DB-CSC: a density-based approach for subspace clustering in graphs with feature vectors
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
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Community can be generally defined as a sub graph where nodes are more densely connected with each other than with the rest of a network. Such definition makes application of density-based clustering methods to community identification justified and natural. Moreover, density-based methods have many extensions enabling their application to complex data analysis. Therefore, the analysis of the characteristics of density-based clustering methods in application to community identification is important and valuable. The article presents and evaluates new similarity measures that can be utilised by the approaches to density-based community identification. Several experiments on real life and generated networks are performed to show and explain the differences between these measures and to compare them with other methods. The results show that the new measures improve the quality of analysis and that density-based clustering algorithms can be valuable community identification methods.