Efficient Substructure Discovery from Large Semi-structured Data

Asai, Tetsuya

doi:10.1137/1.9781611972726.10

Cited by 298 publications

(283 citation statements)

References 15 publications

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“…The next step is similar to the one described before: A collection of reduced call graphs representing correct and failing program executions is analysed with graph mining. The authors use the tree miner FREQT [16] to find all frequent subtrees. The call trees analysed are large and lead to scalability problems of the algorithm.…”

Section: Call Graph Based Fault Detectionmentioning

confidence: 99%

Mining Edge-Weighted Call Graphs to Localise Software Bugs

Eichinger¹,

Böhm²,

Huber³

Machine Learning and Knowledge Discovery in Databases

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An important problem in software engineering is the automated discovery of noncrashing occasional bugs. In this work we address this problem and show that mining of weighted call graphs of program executions is a promising technique. We mine weighted graphs with a combination of structural and numerical techniques. More specifically, we propose a novel reduction technique for call graphs which introduces edge weights. Then we present an analysis technique for such weighted call graphs based on graph mining and on traditional feature selection schemes. The technique generalises previous graph mining approaches as it allows for an analysis of weights. Our evaluation shows that our approach finds bugs which previous approaches cannot detect so far. Our technique also doubles the precision of finding bugs which existing techniques can already localise in principle.

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Section: Call Graph Based Fault Detectionmentioning

confidence: 99%

Mining Edge-Weighted Call Graphs to Localise Software Bugs

Eichinger¹,

Böhm²,

Huber³

Machine Learning and Knowledge Discovery in Databases

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“…, a m , b), (2) whether to be minimal, and (3) whether to be frequent. We note that the edge extension defined by (1) and (2) is also known as rightmost extension, which was originally defined for enumerating frequent subtrees (Asai et al 2002). Practically, the above procedure is done as follows: We first find all frequent 1-edge subgraphs as frequent patterns and save the locations where corresponding graphs are found in given graphs.…”

Section: Reverse Search Reformulation For Enumerating Frequent Subgraphsmentioning

confidence: 99%

Efficiently mining δ-tolerance closed frequent subgraphs

Takigawa

Mamitsuka

2010

Mach Learn

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The output of frequent pattern mining is a huge number of frequent patterns, which are very redundant, causing a serious problem in understandability. We focus on mining frequent subgraphs for which well-considered approaches to reduce the redundancy are limited because of the complex nature of graphs. Two known, standard solutions are closed and maximal frequent subgraphs, but closed frequent subgraphs are still redundant and maximal frequent subgraphs are too specific. A more promising solution is δ-tolerance closed frequent subgraphs, which decrease monotonically in δ, being equal to maximal frequent subgraphs and closed frequent subgraphs for δ = 0 and 1, respectively. However, the current algorithm for mining δ-tolerance closed frequent subgraphs is a naive, two-step approach in which frequent subgraphs are all enumerated and then sifted according to δ-tolerance closedness. We propose an efficient algorithm based on the idea of "reverse-search" by which the completeness of enumeration is guaranteed and for which new pruning conditions are incorporated. We empirically demonstrate that our approach significantly reduced the amount of real computation time of two compared algorithms for mining δ-tolerance closed frequent subgraphs, being pronounced more for practical settings.

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“…Algorithms Several algorithms have been designed to address the problem of tree mining: T reeM iner in [17], F reqT in [1], Chopper [13], F reeT reeM iner [4] and CM T reeM iner [3]. All of them is based on a levelwise process consisting of the following two recursive steps: generation of candidates and validation of candidates.…”

Section: Support(s) = # Of Trees Where S Is Embeddedmentioning

confidence: 99%

“…This research area has several applications, including the discovery of mediator schemas. The background in this research is mainly constituted by the work by Asai et al and Zaki et al [1,9,12,16,17]. This work addresses the problem of tree mining considering several ways to define when a tree S is included within another one T .…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Tree Mining: Go Soft on Your Nodes

Lopez

Laurent

Poncelet³

et al. 2007

Lecture Notes in Computer Science

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Abstract. Tree mining consists in discovering the frequent subtrees from a forest of trees. This problem has many application areas. For instance, a huge volume of data available from the Internet is now described by trees (e.g. XML). Still, for several documents dealing with the same topic, this description is not always the same. It is thus necessary to mine a common structure in order to query these documents. Biology is another field where data may be described by means of trees. The problem of mining trees has now been addressed for several years, leading to well-known algorithms. However, these algorithms can hardly deal with real data in a soft manner. Indeed, they consider a subtree as fully included in the super-tree. This means that all the nodes must appear. In this paper, we extend this definition to fuzzy inclusion based on the idea that a tree is included to a certain degree within another one, this fuzzy degree being correlated to the number of matching nodes.

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Efficient Substructure Discovery from Large Semi-structured Data

Cited by 298 publications

References 15 publications

Mining Edge-Weighted Call Graphs to Localise Software Bugs

Mining Edge-Weighted Call Graphs to Localise Software Bugs

Efficiently mining δ-tolerance closed frequent subgraphs

Fuzzy Tree Mining: Go Soft on Your Nodes

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