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MARGIN: Maximal frequent subgraph mining

MARGIN: Maximal frequent subgraph mining MARGIN: Maximal Frequent Subgraph Mining LINI T. THOMAS, SATYANARAYANA R. VALLURI, and KAMALAKAR KARLAPALEM International Institute of Information Technology, Hyderabad, India The exponential number of possible subgraphs makes the problem of frequent subgraph mining a challenge. The set of maximal frequent subgraphs is much smaller to that of the set of frequent subgraphs providing ample scope for pruning. MARGIN is a maximal subgraph mining algorithm that moves among promising nodes of the search space along the œborder  of the infrequent and frequent subgraphs. This drastically reduces the number of candidate patterns in the search space. The proof of correctness of the algorithm is presented. Experimental results validate the ef ciency and utility of the technique proposed. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications ” Data mining General Terms: Algorithms Additional Key Words and Phrases: Graph mining, maximal frequent subgraph mining ACM Reference Format: Thomas, L. T., Valluri, S. R., and Karlapalem, K. 2010. MARGIN: Maximal frequent subgraph mining. ACM Trans. Knowl. Discov. Data. 4, 3, Article 10 (October 2010), 42 pages. DOI = 10.1145/1839490.1839491 http://doi.acm.org/10.1145/1839490.1839491 1. INTRODUCTION Complex data can be modeled using graphs consisting of nodes and edges that are often labeled to store http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2010 by ACM Inc.
ISSN
1556-4681
DOI
10.1145/1839490.1839491
Publisher site
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Abstract

MARGIN: Maximal Frequent Subgraph Mining LINI T. THOMAS, SATYANARAYANA R. VALLURI, and KAMALAKAR KARLAPALEM International Institute of Information Technology, Hyderabad, India The exponential number of possible subgraphs makes the problem of frequent subgraph mining a challenge. The set of maximal frequent subgraphs is much smaller to that of the set of frequent subgraphs providing ample scope for pruning. MARGIN is a maximal subgraph mining algorithm that moves among promising nodes of the search space along the œborder  of the infrequent and frequent subgraphs. This drastically reduces the number of candidate patterns in the search space. The proof of correctness of the algorithm is presented. Experimental results validate the ef ciency and utility of the technique proposed. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications ” Data mining General Terms: Algorithms Additional Key Words and Phrases: Graph mining, maximal frequent subgraph mining ACM Reference Format: Thomas, L. T., Valluri, S. R., and Karlapalem, K. 2010. MARGIN: Maximal frequent subgraph mining. ACM Trans. Knowl. Discov. Data. 4, 3, Article 10 (October 2010), 42 pages. DOI = 10.1145/1839490.1839491 http://doi.acm.org/10.1145/1839490.1839491 1. INTRODUCTION Complex data can be modeled using graphs consisting of nodes and edges that are often labeled to store

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD)Association for Computing Machinery

Published: Oct 1, 2010

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