The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Ces dernières années, l’extraction d’un ensemble complet de sous-graphes fréquents à partir de données graphiques étiquetées a été étudiée de manière approfondie. Cependant, à notre connaissance, aucune méthode n’a été proposée pour trouver des sous-séquences fréquentes de graphiques à partir d’un ensemble de séquences de graphiques. Dans cet article, nous définissons une nouvelle classe de sous-séquences de graphes en introduisant des règles axiomatiques pour les transformations de graphes, leurs contraintes d'admissibilité et un graphe d'union. Nous proposons ensuite une approche efficace nommée "GTRACE" pour énumérer les sous-séquences de transformation fréquentes (FTS) de graphes à partir d'un ensemble donné de séquences de graphes. Les performances fondamentales de la méthode proposée sont évaluées à l'aide d'ensembles de données artificielles, et sa praticité est confirmée par des expériences utilisant des ensembles de données du monde réel.
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Akihiro INOKUCHI, Takashi WASHIO, "GTRACE: Mining Frequent Subsequences from Graph Sequences" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 10, pp. 2792-2804, October 2010, doi: 10.1587/transinf.E93.D.2792.
Abstract: In recent years, the mining of a complete set of frequent subgraphs from labeled graph data has been studied extensively. However, to the best of our knowledge, no method has been proposed for finding frequent subsequences of graphs from a set of graph sequences. In this paper, we define a novel class of graph subsequences by introducing axiomatic rules for graph transformations, their admissibility constraints, and a union graph. Then we propose an efficient approach named "GTRACE" for enumerating frequent transformation subsequences (FTSs) of graphs from a given set of graph sequences. The fundamental performance of the proposed method is evaluated using artificial datasets, and its practicality is confirmed by experiments using real-world datasets.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2792/_p
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@ARTICLE{e93-d_10_2792,
author={Akihiro INOKUCHI, Takashi WASHIO, },
journal={IEICE TRANSACTIONS on Information},
title={GTRACE: Mining Frequent Subsequences from Graph Sequences},
year={2010},
volume={E93-D},
number={10},
pages={2792-2804},
abstract={In recent years, the mining of a complete set of frequent subgraphs from labeled graph data has been studied extensively. However, to the best of our knowledge, no method has been proposed for finding frequent subsequences of graphs from a set of graph sequences. In this paper, we define a novel class of graph subsequences by introducing axiomatic rules for graph transformations, their admissibility constraints, and a union graph. Then we propose an efficient approach named "GTRACE" for enumerating frequent transformation subsequences (FTSs) of graphs from a given set of graph sequences. The fundamental performance of the proposed method is evaluated using artificial datasets, and its practicality is confirmed by experiments using real-world datasets.},
keywords={},
doi={10.1587/transinf.E93.D.2792},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - GTRACE: Mining Frequent Subsequences from Graph Sequences
T2 - IEICE TRANSACTIONS on Information
SP - 2792
EP - 2804
AU - Akihiro INOKUCHI
AU - Takashi WASHIO
PY - 2010
DO - 10.1587/transinf.E93.D.2792
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2010
AB - In recent years, the mining of a complete set of frequent subgraphs from labeled graph data has been studied extensively. However, to the best of our knowledge, no method has been proposed for finding frequent subsequences of graphs from a set of graph sequences. In this paper, we define a novel class of graph subsequences by introducing axiomatic rules for graph transformations, their admissibility constraints, and a union graph. Then we propose an efficient approach named "GTRACE" for enumerating frequent transformation subsequences (FTSs) of graphs from a given set of graph sequences. The fundamental performance of the proposed method is evaluated using artificial datasets, and its practicality is confirmed by experiments using real-world datasets.
ER -