An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
Akihiro Inokuchi,Takashi Washio,Hiroshi Motoda +2 more
- pp 13-23
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TLDR
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.Abstract:
This paper proposes a novel approach named AGM to efficiently mine the association rules among the frequently appearing substructures in a given graph data set A graph transaction is represented by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through the extended algorithm of the basket analysis Its performance has been evaluated for the artificial simulation data and the carcinogenesis data of Oxford University and NTP Its high efficiency has been confirmed for the size of a real-world problemread more
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Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
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Proceedings ArticleDOI
gSpan: graph-based substructure pattern mining
Xifeng Yan,Jiawei Han +1 more
TL;DR: 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.
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Frequent pattern mining: current status and future directions
TL;DR: It is believed that frequent pattern mining research has substantially broadened the scope of data analysis and will have deep impact on data mining methodologies and applications in the long run, however, there are still some challenging research issues that need to be solved before frequent patternmining can claim a cornerstone approach in data mining applications.
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.
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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Fast Algorithms for Mining Association Rules in Large Databases
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Data mining and knowledge discovery: making sense out of data
TL;DR: Without a concerted effort to develop knowledge discovery techniques, organizations stand to forfeit much of the value from the data they currently collect and store.
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Levelwise Search and Borders of Theories in KnowledgeDiscovery
Heikki Mannila,Hannu Toivonen +1 more
TL;DR: The concept of the border of a theory, a notion that turns out to be surprisingly powerful in analyzing the algorithm, is introduced and strong connections between the verification problem and the hypergraph transversal problem are shown.
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Substructure discovery using minimum description length and background knowledge
Diane J. Cook,Lawrence B. Holder +1 more
TL;DR: A new version of the SUBDUE substructure discovery system based on the minimum description length principle is described, which discovers substructures that compress the original data and represent structural concepts in the data.