Frequent pattern mining in attributed trees: algorithms and applications

Pasquier, Claude; Sanhes, Jérémy; Flouvat, Frédéric; Selmaoui-Folcher, Nazha

doi:10.1007/s10115-015-0831-x

Cited by 15 publications

(8 citation statements)

References 31 publications

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“…However, finding large subgraphs is sometimes unnecessary for decision-making. Hence, special cases of the FSM problem that are easier to solve have been studied, such as mining frequent trees [14], [15] and paths [16], [17]. But many graph mining algorithms can only handle graphs with a single label per node.…”

Section: Related Workmentioning

confidence: 99%

Discovering Representative Attribute-stars via Minimum Description Length

Liu¹,

Zhou²,

Fournier‐Viger³

et al. 2022

Preprint

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Graphs are a popular data type found in many domains. Numerous techniques have been proposed to find interesting patterns in graphs to help understand the data and support decision-making. However, there are generally two limitations that hinder their practical use: (1) they have multiple parameters that are hard to set but greatly influence results, (2) and they generally focus on identifying complex subgraphs while ignoring relationships between attributes of nodes.Graphs are a popular data type found in many domains. Numerous techniques have been proposed to find interesting patterns in graphs to help understand the data and support decision-making. However, there are generally two limitations that hinder their practical use: (1) they have multiple parameters that are hard to set but greatly influence results, (2) and they generally focus on identifying complex subgraphs while ignoring relationships between attributes of nodes. To address these problems, we propose a parameter-free algorithm named CSPM (Compressing Star Pattern Miner) which identifies star-shaped patterns that indicate strong correlations among attributes via the concept of conditional entropy and the minimum description length principle. Experiments performed on several benchmark datasets show that CSPM reveals insightful and interpretable patterns and is efficient in runtime. Moreover, quantitative evaluations on two real-world applications show that CSPM has broad applications as it successfully boosts the accuracy of graph attribute completion models by up to 30.68% and uncovers important patterns in telecommunication alarm data.

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Section: Related Workmentioning

confidence: 99%

Discovering Representative Attribute-stars via Minimum Description Length

Liu¹,

Zhou²,

Fournier‐Viger³

et al. 2022

Preprint

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show abstract

“…But the former is a tractable problem while the latter is intractable. Recently, the problem of frequent tree mining has been generalized as frequently attributed tree mining to consider trees and subtrees where each node may have multiple labels ( attributed trees ; (Pasquier et al, 2016).…”

Section: Mining Patterns In a Static Graph Databasementioning

confidence: 99%

A survey of pattern mining in dynamic graphs

Fournier-Viger

Cheng

et al. 2020

WIREs Data Min & Knowl

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Graph data is found in numerous domains such as for the analysis of social networks, sensor networks, bioinformatics, industrial systems, and chemistry. Analyzing graphs to identify useful and interesting patterns is an important research area. It helps understanding graphs, and hence support decision making. Since two decades, many graph mining algorithms have been proposed to identify patterns such as frequent subgraphs, paths, cliques, and trees. But most of them assume that graphs are static. This simplifying assumption makes it easy to design algorithms but discard information about how graphs evolve. This article provides a detailed survey of techniques for mining interesting patterns in dynamic graphs, which can serve both as an introduction and as a guide to recent advances and opportunities in this research area. The main tasks related to mining patterns in dynamic graphs are reviewed such as discovering frequent subgraphs, evolution rules, motifs, subgraph sequences, recurrent and triggering patterns, and trend sequences. In addition, an overview of strategies and approaches to solve dynamic graph mining problems is presented, and their advantages and limitations are highlighted. Various extensions are also discussed such as to discover patterns in data streams and big data. Finally, the article mentions several research opportunities.

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“…Our work addresses this weakness by showing how pattern mining algorithms can indeed find useful patterns, as validated by software engineers. A notable exception is an algorithm that operates on attributed trees [7]. Our trees are not attributed, and hence we could not apply this algorithm.…”

Section: Related Workmentioning

confidence: 99%

Mining Patterns in Source Code Using Tree Mining Algorithms

et al. 2019

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Discovering regularities in source code is of great interest to software engineers, both in academia and in industry, as regularities can provide useful information to help in a variety of tasks such as code comprehension, code refactoring, and fault localisation. However, traditional pattern mining algorithms often find too many patterns of little use and hence are not suitable for discovering useful regularities. In this paper we propose FREQTALS, a new algorithm for mining patterns in source code based on the FREQT tree mining algorithm. First, we introduce several constraints that effectively enable us to find more useful patterns; then, we show how to efficiently include them in FREQT. To illustrate the usefulness of the constraints we carried out a case study in collaboration with software engineers, where we identified a number of interesting patterns in a repository of Java code.

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Frequent pattern mining in attributed trees: algorithms and applications

Cited by 15 publications

References 31 publications

Discovering Representative Attribute-stars via Minimum Description Length

Discovering Representative Attribute-stars via Minimum Description Length

A survey of pattern mining in dynamic graphs

Mining Patterns in Source Code Using Tree Mining Algorithms

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