Clustering in Attributed Graphs
The aim of data mining approaches is to extract novel knowledge from large sets of data. These data can be represented in different manners: high-dimensional attribute data to characterize single objects and graph data to represent the relations between objects. While the first data type is analyzed by subspace clustering approaches, the second one is analyzed by dense subgraph clustering methods. For many applications both types of data (attributes and relationships) are available and can be modeled as graphs with attributed nodes. Analyzing both data sources simultaneously can increase the quality of mining methods. However, most clustering approaches deal only with one of these data types. In our works, we develop novel methods that use both data types simultaneously and thereby obtain better clustering results.
Simultaneous mining of dense subgraphs and subspace clusters in attributed graphs