ABSTRACT
Mining frequent structural patterns from graph databases is an important research problem with broad applications. Recently, we developed an effective index structure, ADI, and efficient algorithms for mining frequent patterns from large, disk-based graph databases [5], as well as constraint-based mining techniques. The techniques have been integrated into a research prototype system--- GraphMiner. In this paper, we describe a demo of GraphMiner which showcases the technical details of the index structure and the mining algorithms including their efficient implementation, the mining performance and the comparison with some state-of-the-art methods, the constraint-based graph-pattern mining techniques and the procedure of constrained graph mining, as well as mining real data sets in novel applications.
- C. Borgelt and M. R. Berthold. Mining molecular fragments: Finding relevant substructures of molecules. In ICDM'02, Maebashi TERRSA, Maebashi City, Japan, Dec. 2002. Google ScholarDigital Library
- A. Inokuchi, T. Washio, and H. Motoda. An apriori-based algorithm for mining frequent substructures from graph data. In PKDD'00, pages 13--23, Lyon, France, Sept. 2000.Google ScholarDigital Library
- M. Kuramochi and G. Karypis. Frequent subgraph discovery. In ICDM'01, pages 313--320, San Jose, CA, Nov. 2001. Google ScholarDigital Library
- N. Vanetik, E. Gudes, and S. E. Shimony. Computing frequent graph patterns from semistructured data. In ICDM'02, Maebashi TERRSA, Maebashi City, Japan, Dec. 2002. Google ScholarDigital Library
- C. Wang, W. Wang, J. Pei, Y. Zhu, and B. Shi. Scalable mining of large disk-base graph databases. In KDD'04, pages 316--325. ACM Press, 2004. Google ScholarDigital Library
- X. Yan and J. Han. Closegraph: Mining closed frequent graph patterns. In KDD'03, Washington, D. C, 2003. ACM Press. Google ScholarDigital Library
- Y. Yan and J. Han. gspan: Graph-based substructure pattern mining. In ICDM'02, Maebashi, Japan, December 2002. Google ScholarDigital Library
- GraphMiner: a structural pattern-mining system for large disk-based graph databases and its applications
Recommendations
Efficient algorithms for mining high-utility itemsets in uncertain databases
High-utility itemset mining (HUIM) is a useful set of techniques for discovering patterns in transaction databases, which considers both quantity and profit of items. However, most algorithms for mining high-utility itemsets (HUIs) assume that the ...
Efficient algorithms for mining constrained frequent patterns from uncertain data
U '09: Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain DataMining of frequent patterns is one of the popular knowledge discovery and data mining (KDD) tasks. It also plays an essential role in the mining of many other patterns such as correlation, sequences, and association rules. Hence, it has been the subject ...
A method for mining top-rank-k frequent closed itemsets
Collective intelligent information and database systemsMining frequent closed itemsets (FCIs) is important in mining non-redundant (minimal) association rules. Therefore, many algorithms have been developed for mining FCIs with reduced mining time and memory usage. For mining FCIs, algorithms use the minimum ...
Comments