The Gaston Tool for Frequent Subgraph Mining

Nijssen, Siegfried; Kok, Joost N.

doi:10.1016/j.entcs.2004.12.039

Cited by 129 publications

(104 citation statements)

References 8 publications

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“…However, we discovered five unconnected graphs in the dataset and removed them, as the cohesive itemset approach can only be applied to connected graphs, leaving us with 335 input graphs. We compare our algorithm for finding cohesive itemsets in multiple graphs with the Gaston tool developed by Nijssen and Kok [15]. Both the dataset and the implementation are available online 2 .…”

Section: Multiple Graph Settingmentioning

confidence: 99%

“…Yan and Han [19] proposed gspan, which performs a depth-first search, based on a pattern growth principle similar to the one used in Fp-growth [7] for mining itemsets. Nijssen et al proposed a more efficient frequent subgraph mining tool, called Gaston, which finds the frequent substructures in a number of phases of increasing complexity [15]. More specifically, it first searches for frequent paths, then frequent free trees and finally cyclic graphs.…”

Section: Related Workmentioning

confidence: 99%

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Mining Interesting Itemsets in Graph Datasets

Čule

Goethals

Hendrickx

2013

Advances in Knowledge Discovery and Data Mining

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Abstract. Traditionally, pattern discovery in graphs has been mostly limited to searching for frequent subgraphs, reoccurring patterns within which nodes with certain labels are frequently interconnected in exactly the same way. We relax this requirement by claiming that a set of labels is interesting if they often occur in each other's vicinity, but not necessarily always interconnected by exactly the same structures. Searching for such itemsets can be done both in datasets consisting of one large graph, and in datasets consisting of many graphs. We present novel methods dealing with both possible settings. Our algorithms benefit from avoiding computationally costly isomorphism checks typical for subgraph mining, as well as from a greatly reduced search space, as we consider only itemsets, and not all possible edges and paths that can connect them.

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Section: Multiple Graph Settingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Mining Interesting Itemsets in Graph Datasets

Čule

Goethals

Hendrickx

2013

Advances in Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

show abstract

“…A subgraph is frequent if it exists in at least t transactions, where t is a user-defined threshold. Some of the representative works in this domain are AGM [45], gSpan [23], CloseGraph [46], SPIN [47], Gaston [24], and Mofa [46]. On the other hand, in case of a single large graph, a subgraph is frequent if it has at least t appearances.…”

Section: Graph Pattern Miningmentioning

confidence: 99%

“…Table 1 shows the selected algorithms and their characteristics. We use gSpan [23], Gaston [24], and kFISM [25] to mine graph database. The gSpan finds patterns whose occurrence count satisfies the minimum support threshold.…”

Section: Graph Pattern Miningmentioning

confidence: 99%

LPaMI: A Graph-Based Lifestyle Pattern Mining Application Using Personal Image Collections in Smartphones

et al. 2017

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Normally, individuals use smartphones for a variety of purposes like photography, schedule planning, playing games, and so on, apart from benefiting from the core tasks of call-making and short messaging. These services are sources of personal data generation. Therefore, any application that utilises personal data of a user from his/her smartphone is truly a great witness of his/her interests and this information can be used for various personalised services. In this paper, we present Lifestyle Pattern MIning (LPaMI), which is a personalised application for mining the lifestyle patterns of a smartphone user. LPaMI uses the personal photograph collections of a user, which reflect the day-to-day photos taken by a smartphone, to recognise scenes (called objects of interest in our work). These are then mined to discover lifestyle patterns. The uniqueness of LPaMI lies in our graph-based approach to mining the patterns of interest. Modelling of data in the form of graphs is effective in preserving the lifestyle behaviour maintained over the passage of time. Graph-modelled lifestyle data enables us to apply variety of graph mining techniques for pattern discovery. To demonstrate the effectiveness of our proposal, we have developed a prototype system for LPaMI to implement its end-to-end pipeline. We have also conducted an extensive evaluation for various phases of LPaMI using different real-world datasets. We understand that the output of LPaMI can be utilised for variety of pattern discovery application areas like trip and food recommendations, shopping, and so on.

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“…One, Treeminer, uses equivalence class based extensions to effectively discover frequently embedded subtrees [24]. Instead, GASTON divides the frequent subgraph mining process into path mining, then subtree mining, and finally subgraph mining [19]. We use SLEUTH, an opensource frequent subtree miner, able to efficiently mine frequent, unordered or ordered, embedded or induced subtrees in a library of labeled trees [23].…”

Section: Related Workmentioning

confidence: 99%

Graph-Based Task Libraries for Robots: Generalization and Autocompletion

Klee

Gemignani

Nardi

et al. 2015

Lecture Notes in Computer Science

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Abstract. In this paper, we consider an autonomous robot that persists over time performing tasks and the problem of providing one additional task to the robot's task library. We present an approach to generalize tasks, represented as parameterized graphs with sequences, conditionals, and looping constructs of sensing and actuation primitives. Our approach performs graph-structure task generalization, while maintaining task executability and parameter value distributions. We present an algorithm that, given the initial steps of a new task, proposes an autocompletion based on a recognized past similar task. Our generalization and autocompletion contributions are effective on different real robots. We show concrete examples of the robot primitives and task graphs, as well as results, with Baxter. In experiments with multiple tasks, we show a significant reduction in the number of new task steps to be provided.

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The Gaston Tool for Frequent Subgraph Mining

Cited by 129 publications

References 8 publications

Mining Interesting Itemsets in Graph Datasets

Mining Interesting Itemsets in Graph Datasets

LPaMI: A Graph-Based Lifestyle Pattern Mining Application Using Personal Image Collections in Smartphones

Graph-Based Task Libraries for Robots: Generalization and Autocompletion

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