Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
IEEE Intelligent Systems
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
DB-FSG: An SQL-Based Approach for Frequent Subgraph Mining
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Mining globally distributed frequent subgraphs in a single labeled graph
Data & Knowledge Engineering
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Frequent subgraph mining (FSG) has always been an important issue in data mining. Several frequent subgraph mining methods have been developed for mining graph data. However, most of these are main memory algorithms in which scalability is a bigger issue. A few algorithms have opted for a relational approach that stores the graph data in relational tables. However, relational databases have their own space as well computing constraints when it comes to storing large databases. Moreover, relational databases do not preserve semantic information as they represent simple entities and in order to preserve the relationship between two entities additional tables are necessary. Object-oriented databases, on the other hand, do not have these constraints. In this paper, we present an object-oriented database approach to mining frequent sub-graphs. We use Db4o, a popular open-source object database system, to store the input graph data as well as intermediate results. Db4o can save all the information about an entity in a single class in an object form. Application domains such as protein-protein interaction data, social network data, and chemical compound structure data require mining frequent subgraphs while preserving the meaning. This paper proposes a novel idea for using object oriented database db4o to store graph data, which can support large graph data as well as preserve semantic information.