Self-organizing maps
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
A Comparison of Algorithms for Maximum Common Subgraph on Randomly Connected Graphs
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Validation indices for graph clustering
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Clustering graphs by weighted substructure mining
ICML '06 Proceedings of the 23rd international conference on Machine learning
Xproj: a framework for projected structural clustering of xml documents
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph clustering based on structural similarity of fragments
Proceedings of the 2005 international conference on Federation over the Web
Parallel structural graph clustering
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
The augmented itemset tree: a data structure for online maximum frequent pattern mining
DS'11 Proceedings of the 14th international conference on Discovery science
FXProj: a fuzzy XML documents projected clustering based on structure and content
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
A structural cluster kernel for learning on graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
| Hi-index | 0.00 |
The goal of graph clustering is to partition objects in a graph database into different clusters based on various criteria such as vertex connectivity, neighborhood similarity or the size of the maximum common subgraph. This can serve to structure the graph space and to improve the understanding of the data. In this paper, we present a novel method for structural graph clustering, i.e. graph clustering without generating features or decomposing graphs into parts. In contrast to many related approaches, the method does not rely on computationally expensive maximum common subgraph (MCS) operations or variants thereof, but on frequent subgraph mining. More specifically, our problem formulation takes advantage of the frequent subgraph miner gSpan (that performs well on many practical problems) without effectively generating thousands of subgraphs in the process. In the proposed clustering approach, clusters encompass all graphs that share a sufficiently large common subgraph. The size of the common subgraph of a graph in a cluster has to take at least a user-specified fraction of its overall size. The new algorithm works in an online mode (processing one structure after the other) and produces overlapping (non-disjoint) and nonexhaustive clusters. In a series of experiments, we evaluated the effectiveness and efficiency of the structural clustering algorithm on various real world data sets of molecular graphs.