An efficient algorithm of frequent connected subgraph extraction

Authors:
Mingsheng Hong;Haofeng Zhou;Wei Wang;Baile Shi
Affiliations:
Department of Computing and Information Technology Science, Fudan University, Shanghai, P.R. China;Department of Computing and Information Technology Science, Fudan University, Shanghai, P.R. China;Department of Computing and Information Technology Science, Fudan University, Shanghai, P.R. China;Department of Computing and Information Technology Science, Fudan University, Shanghai, P.R. China
Venue:
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2003

Citing 17
Cited 3

SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules

Journal of Chemical Information & Computer Sciences
Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
CLIP: concept learning from inference patterns

Artificial Intelligence - Special issue: AI research in Japan
An effective hash-based algorithm for mining association rules

SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Molecular feature mining in HIV data

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Finding Patterns in Three-Dimensional Graphs: Algorithms and Applications to Scientific Data Mining

IEEE Transactions on Knowledge and Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth

Proceedings of the 17th International Conference on Data Engineering
Frequent Subgraph Discovery

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
Knowledge Discovery from Structured Data by Beam-Wise Graph-Based Induction

PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Canonical labeling of graphs

STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
gSpan: Graph-Based Substructure Pattern Mining

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Substructure discovery using minimum description length and background knowledge

Journal of Artificial Intelligence Research
Machine learning techniques to make computers easier to use

IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
The levelwise version space algorithm and its application to molecular fragment finding

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2

Finding Frequent Patterns in a Large Sparse Graph*

Data Mining and Knowledge Discovery
Discovering Frequent Graph Patterns Using Disjoint Paths

IEEE Transactions on Knowledge and Data Engineering
MapReduce-Based Pattern Finding Algorithm Applied in Motif Detection for Prescription Compatibility Network

APPT '09 Proceedings of the 8th International Symposium on Advanced Parallel Processing Technologies

Quantified Score

Hi-index	0.00

Visualization

Abstract

Mining frequent patterns from datasets is one of the key success stories of data mining research. Currently, most of the works focus on independent data, such as the items in the marketing basket. However, the objects in the real world often have close relationship with each other. How to extract frequent patterns from these relations is the objective in this paper. We use graphs to model the relations, and select a simple type for analysis. Combining the graph theory and algorithms to generate frequent patterns, a new algorithm Topology, which can mine these graphs efficiently, has been proposed. We evaluate the performance of the algorithm by doing experiments with synthetic datasets and real data. The experimental results show that Topology can do the job well. At the end of this paper, the potential improvement is mentioned.