Proceedings ArticleDOI
gSpan: graph-based substructure pattern mining
Xifeng Yan,Jiawei Han +1 more
- pp 721-724
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TLDR
A novel algorithm called gSpan (graph-based substructure pattern mining), which discovers frequent substructures without candidate generation by building a new lexicographic order among graphs, and maps each graph to a unique minimum DFS code as its canonical label.Abstract:
We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based substructure pattern mining), 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. Based on this lexicographic order gSpan adopts the depth-first search strategy to mine frequent connected subgraphs efficiently. Our performance study shows that gSpan substantially outperforms previous algorithms, sometimes by an order of magnitude.read more
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References
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Proceedings Article
Fast algorithms for mining association rules
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
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Introduction to algorithms: 4. Turtle graphics
TL;DR: In this article, a language similar to logo is used to draw geometric pictures using this language and programs are developed to draw geometrical pictures using it, which is similar to the one we use in this paper.
Proceedings ArticleDOI
PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth
TL;DR: This work proposes a novel sequential pattern mining method, called Prefixspan (i.e., Prefix-projected - Ettern_ mining), which explores prejxprojection in sequential pattern Mining, and shows that Pre fixspan outperforms both the Apriori-based GSP algorithm and another recently proposed method; Frees pan, in mining large sequence data bases.
Proceedings ArticleDOI
Frequent subgraph discovery
TL;DR: The empirical results show that the algorithm scales linearly with the number of input transactions and it is able to discover frequent subgraphs from a set of graph transactions reasonably fast, even though it has to deal with computationally hard problems such as canonical labeling of graphs and subgraph isomorphism which are not necessary for traditional frequent itemset discovery.
Book ChapterDOI
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
TL;DR: A novel approach named AGM to efficiently mine the association rules among the frequently appearing substructures in a given graph data set through the extended algorithm of the basket analysis is proposed.