Diagonally subgraphs pattern mining

Moti Cohen, Ehud Gudes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

In this paper we present an efficient algorithm, called DSPM, for mining all frequent subgraphs in large set of graphs. The algorithm explores the search space in a DFS fashion, while generating candidates in advance to each mining phase just like the Apriori algorithm does. It combines the candidate generation and anti monotone pruning into one efficient operation thanks to the unique mode of exploration. DSPM efficiently enumerates all frequent patterns by using diagonal search, which is a general scheme for designing effective algorithms for hard enumeration problems. Our experiments show that DSPM has better performance, from several aspects, than the current state of the art - gSpan algorithm.

Original languageEnglish
Title of host publicationWorkshop Proceedings - The 9th Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International Conference on Management of Data, SIGMOD-04
Pages51-58
Number of pages8
DOIs
StatePublished - 1 Dec 2004
Event9th Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International Conference on Management of Data, SIGMOD-04 - Paris, France
Duration: 13 Jun 200413 Jun 2004

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Conference

Conference9th Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International Conference on Management of Data, SIGMOD-04
Country/TerritoryFrance
CityParis
Period13/06/0413/06/04

Keywords

  • frequent subgraphs
  • graph mining
  • pattern discovery

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