Theoretical Analysis and Experimental Comparison of Graph Matching Algorithms for Database Filtering

Irniger, Christophe; Bunke, Horst

doi:10.1007/3-540-45028-9_11

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…A graphical illustration is shown in Figure 1. The presented work extends previous studies on graph matching performance and graph database filtering (see [11,12]). In contrast with the work reported in [11], which was restricted to the case of graph isomorphism, the present paper deals with the problem of subgraph-isomorphism.…”

Section: Introductionsupporting

confidence: 84%

“…Database filtering in conjunction with subgraph-isomorphism search was also studied in [13]. However, the decision trees used in [13] were identical to the decision trees used in [11,12] for graph-isomorphism, and the case of subgraph-isomorphism was dealt with by means of an extended decision tree traversal procedure. By contrast, a generalized decision tree induction procedure is proposed in the present paper.…”

Section: Introductionmentioning

confidence: 99%

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Decision Tree Structures for Graph Database Filtering

Irniger

Bunke

2004

Lecture Notes in Computer Science

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Abstract. In structural pattern recognition it is often required to match an unknown sample against a database of candidate patterns in order to find the most similar prototype. If the patterns are represented using graphs, the sample's graph is matched against a database of model graphs and the pattern recognition problem is turned into a graph matching problem. Graph matching is a powerful yet computationally expensive procedure. If the unknown sample is matched against a whole database of prototypes, the size of the database is introduced as an additional factor into the overall complexity of the matching process. To reduce the influence of that factor an approach based on machine learning techniques is proposed in this paper. The graphs are represented using feature vectors. Based on these vectors a decision tree is built to index the database. The decision tree allows at runtime to eliminate a number of graphs from the database as possible matching candidates. Experimental results are reported demonstrating the efficiency of the proposed filtering procedure. The work presented in this paper extends previous studies from the case of graph-isomorphism to the problem of subgraph-isomorphism.

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Section: Introductionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Decision Tree Structures for Graph Database Filtering

Irniger

Bunke

2004

Lecture Notes in Computer Science

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“…The purpose of such scheme is to optimize the graph matching procedure. In [8] one important highlighted aspect, which is widely neglected; that the "non-matching symbol" matching time in any algorithms plays a crucial role in determining the performance of the graph-matching algorithm. So for large databases, a small number of expected matches and no filtering would definitely degrade the performance of any graph-matching algorithm by matching the "non-matching symbol" from the database.…”

Section: Error-correcting Graph Matchingmentioning

confidence: 99%

“…So for large databases, a small number of expected matches and no filtering would definitely degrade the performance of any graph-matching algorithm by matching the "non-matching symbol" from the database. In [8] number of parameters are identified that affect the performance of any graph matching algorithm such as total database size, size of database after filtering, number of matches in the database and time needed for filtering. Graph matching is a quite expensive procedure.…”

Section: Error-correcting Graph Matchingmentioning

confidence: 99%

“…If the model symbol database is large, then it adds more complexity in the matching procedure. In [8] database filtering procedures are used to reduce the overall cost of graph matching. The database filtering reduces the number of feasible candidates and then tests the remaining candidates with the conventional graph matching algorithms.…”

Section: Error-correcting Graph Matchingmentioning

confidence: 99%

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Graph based pattern matching

Pawar¹,

Zaveri

2011

2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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The Graphs are very powerful and widely used tool for data representation in various fields of science and engineering. Due to their versatile representational power graphs are widely used for dealing with structural information in different domains such as pattern recognition, computer vision, networks, biochemical applications, psycho-sociology, image interpretation, and many others. In many applications, it is necessary to find the similarity between objects. When graphs are used for representation of structured objects, then the problem of measuring object similarity converts into the problem of determining the similarity of graphs, which is also known as graph matching. In order to achieve a good degree of graph matching the most usual way is to search for a graph isomorphism. A lot of work has been done in the area of graph isomorphism between two graphs or sub-graphs. Depending on the nature of the application, the problem turns to either exact or inexact graph isomorphism. In this study work, we are going to discuss the important theoretical foundations of graph matching along with the error-correcting graph matching approach.

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Graph-Based Tools for Data Mining and Machine Learning

Bunke

Machine Learning and Data Mining in Pattern Recognition

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Theoretical Analysis and Experimental Comparison of Graph Matching Algorithms for Database Filtering

Cited by 12 publications

References 9 publications

Decision Tree Structures for Graph Database Filtering

Decision Tree Structures for Graph Database Filtering

Graph based pattern matching

Graph-Based Tools for Data Mining and Machine Learning

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