2012 SummarizationbasedMiningBiparti

• (Feng et al., 2012) ⇒ Jing Feng, Xiao He, Bettina Konte, Christian Böhm, and Claudia Plant. (2012). “Summarization-based Mining Bipartite Graphs.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339725

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Summarization-based+Mining+Bipartite+Graphs
• http://dl.acm.org/citation.cfm?id=2339530.2339725&preflayout=flat#citedby

Quotes

Author Keywords

• Bipartite graph; clustering; data mining; link prediction; summarization

Abstract

How to extract the truly relevant information from a large relational data set? The answer of this paper is a technique integrating graph summarization, graph clustering, link prediction and the discovery of the hidden structure on the basis of data compression. Our novel algorithm SCMiner (for Summarization-Compression Miner) reduces a large bipartite input graph to a highly compact representation which is very useful for different data mining tasks: 1) Clustering: The compact summary graph contains the truly relevant clusters of both types of nodes of a bipartite graph. 2) Link prediction: The compression scheme of SCMiner reveals suspicious edges which are probably erroneous as well as missing edges, i.e. pairs of nodes which should be connected by an edge. 3) Discovery of the hidden structure: Unlike traditional co-clustering methods, the result of SCMiner is not limited to row - and column-clusters. Besides the clusters, the summary graph also contains the essential relationships between both types of clusters and thus reveals the hidden structure of the data. Extensive experiments on synthetic and real data demonstrate that SCMiner outperforms state-of-the-art techniques for clustering and link prediction. Moreover, SCMiner discovers the hidden structure and reports it in an interpretable way to the user. Based on data compression, our technique does not rely on any input parameters which are difficult to estimate.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 SummarizationbasedMiningBiparti	Christian Böhm Claudia Plant Jing Feng Xiao He Bettina Konte			Summarization-based Mining Bipartite Graphs				10.1145/2339530.2339725		2012