AI helps you reading Science

AI generates interpretation videos

AI extracts and analyses the key points of the paper to generate videos automatically


pub
Go Generating

AI Traceability

AI parses the academic lineage of this thesis


Master Reading Tree
Generate MRT

AI Insight

AI extracts a summary of this paper


Weibo:
We introduced a new lexicographic ordering system and developed a depth-first search-based mining algorithm gSpan for efficient mining of frequent subgraphs in large graph database

gSpan: Graph-Based Substructure Pattern Mining

ICDM, pp.721-721, (2002)

Cited by: 2743|Views223
EI

Abstract

We investigate new approaches for frequent graph-basedpattern mining in graph datasets and propose a novel algorithmcalled gSpan (graph-based Substructure pattern mining),which discovers frequent substructures without candidategeneration. gSpan builds a new lexicographic orderamong graphs, and maps each graph to a unique minimumDFS code a...More

Code:

Data:

0
Introduction
  • Frequent substructure pattern mining has been an emerging data mining problem with many scientific and commercial applications.
  • 6 of graphs in which is a subgraph.
  • 6 of frequent subgraph mining is to find any subgraph s.t.
  • To reduce the complexity of the problem, only frequent connected subgraphs are studied in this paper.
  • The kernel of frequent subgraph mining is subgraph isomorphism test.
  • The frequent subgraph mining problem was not explored well.
  • Kuramochi and Karypis [5]
Highlights
  • Frequent substructure pattern mining has been an emerging data mining problem with many scientific and commercial applications
  • To reduce the complexity of the problem, only frequent connected subgraphs are studied in this paper
  • We introduced a new lexicographic ordering system and developed a depth-first search-based mining algorithm gSpan for efficient mining of frequent subgraphs in large graph database
  • Our performance study shows that gSpan outperforms FSG by an order of magnitude and is capable to mine large frequent subgraphs in a bigger graph set with lower minimum supports than previous studies
Conclusion
  • The authors introduced a new lexicographic ordering system and developed a depth-first search-based mining algorithm gSpan for efficient mining of frequent subgraphs in large graph database.
  • The authors' performance study shows that gSpan outperforms FSG by an order of magnitude and is capable to mine large frequent subgraphs in a bigger graph set with lower minimum supports than previous studies
Tables
  • Table1: DFS codes for Fig. 1(b)-(d)
Download tables as Excel
Funding
  • Investigates new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan, which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and maps each graph to a unique minimum DFS code as its canonical label
  • Develops gSpan, which targets to reduce or avoid the significant costs mentioned above
Reference
  • R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In VLDB’94, pages 487–499, Sept. 1994.
    Google ScholarLocate open access versionFindings
  • T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Satamoto, and S. Arikawa. Efficient substructure discovery from large semistructured data. In SIAM SDM’02, April 2002.
    Google ScholarLocate open access versionFindings
  • T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to Algorithms. MIT Press, 2001, Second Edition.
    Google ScholarFindings
  • A. Inokuchi, T. Washio, and H. Motoda. An apriori-based algorithm for mining frequent substructures from graph data. In PKDD’00, pages 13–23, 2000.
    Google ScholarLocate open access versionFindings
  • M. Kuramochi and G. Karypis. Frequent subgraph discovery. In ICDM’01, pages 313–320, Nov. 2001.
    Google ScholarLocate open access versionFindings
  • J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In ICDE’01, pages 215–224, April 2001.
    Google ScholarLocate open access versionFindings
  • X. Yan and J. Han. gspan: Graph-based substructure pattern mining. Technical Report UIUCDCS-R-2002-2296, Department of Computer Science, University of Illinois at UrbanaChampaign, 2002.
    Google ScholarFindings
  • M. J. Zaki. Efficiently mining frequent trees in a forest. In KDD’02, July 2002.
    Google ScholarLocate open access versionFindings
Your rating :
0

 

Tags
Comments
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn
小科