An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th 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
Scalable mining of large disk-based graph databases
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Partition-Based Approach to Graph Mining
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Improvements in the data partitioning approach for frequent itemsets mining
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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Existing graph mining algorithms typically assume that the dataset can fit into main memory. As many large graph datasets cannot satisfy this condition, truly scalable graph mining remains a challenging computational problem. In this paper, we present a new horizontal data partitioning framework for graph mining. The original dataset is divided into fragments, then each fragment is mined individually and the results are combined together to generate a global result. One of the challenging problems in graph mining is about the completeness because the of complexity graph structures. We will prove the completeness of our algorithm in this paper. The experiments will be conducted to illustrate the efficiency of our data partitioning approach.