An Error-Tolerant Approximate Matching Algorithm for Attributed Planar Graphs and Its Application to Fingerprint Classification

Neuhaus, Michel; Bunke, Horst

doi:10.1007/978-3-540-27868-9_18

Cited by 59 publications

(34 citation statements)

References 21 publications

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“…Of the algorithms that we tested, the greedy algorithm and the exhaustive algorithm with pruning are general algorithms to solve recursive problems. The process heuristic algorithm is similar to Neuhaus and Bunke's planar graph matching algorithm [17]. The main difference is that their algorithm starts with a random pair of graph nodes for comparison, while we assume that business process models have source nodes and sink nodes and we start by mapping source nodes.…”

Section: Related Workmentioning

confidence: 99%

Graph Matching Algorithms for Business Process Model Similarity Search

Dijkman

Dumas

García‐Bañuelos

2009

Lecture Notes in Computer Science

295

243

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Abstract. We investigate the problem of ranking all process models in a repository according to their similarity with respect to a given process model. We focus specifically on the application of graph matching algorithms to this similarity search problem. Since the corresponding graph matching problem is NP-complete, we seek to find a compromise between computational complexity and quality of the computed ranking. Using a repository of 100 process models, we evaluate four graph matching algorithms, ranging from a greedy one to a relatively exhaustive one. The results show that the mean average precision obtained by a fast greedy algorithm is close to that obtained with the most exhaustive algorithm.

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Section: Related Workmentioning

confidence: 99%

Graph Matching Algorithms for Business Process Model Similarity Search

Dijkman

Dumas

García‐Bañuelos

2009

Lecture Notes in Computer Science

295

243

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“…In particular, we plan to consider other graph matching techniques in which grouping of activities could be done more effectively. One possibility is to adapt a technique for graph matching of planar graphs proposed by Neuhaus and Bunke [18], in which a mapping is constructured starting from a node and then exploring the neighbourhood of that node, at which point one could consider grouping activities together. Process models are generally planar graphs (particularly structured process models) and this property can be exploited to achieve better tradeoffs.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Merging Business Process Models

Rosa

Dumas

Uba

et al. 2010

On the Move to Meaningful Internet Systems: OTM 2010

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Abstract-This paper studies the following problem: given a pair of business process models, determine which elements in one model are related to which elements in the other model. This problem arises in the context of merging different versions or variants of a business process model or when comparing business process models in order to display their similarities and differences. The paper investigates two approaches to this alignment problem: one based purely on lexical matching of pairs of elements and another based on error-correcting graph matching. Using a set of models taken from real-life scenarios, the paper empirically shows that graph matching techniques yield a significantly higher precision than pure lexical matching, while achieving comparable recall.

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“…As an alternative, a number of suboptimal methods have been proposed to make the graph edit distance less computationally demanding and therefore usable in real applications. Some of these methods are based on local optimization [24]. A linear programming method to compute the graph edit distance with unlabelled edges is presented in [25].…”

Section: Graph Edit Distancementioning

confidence: 99%

A generic framework for median graph computation based on a recursive embedding approach

Ferrer

Karatzas

Valveny

et al. 2011

Computer Vision and Image Understanding

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The median graph has been shown to be a good choice to obtain a representative of a set of graphs. However, its computation is a complex problem. Recently, graph embedding into vector spaces has been proposed to obtain approximations of the median graph. The problem with such an approach is how to go from a point in the vector space back to a graph in the graph space. The main contribution of this paper is the generalization of this previous method, proposing a generic recursive procedure that permits to recover the graph corresponding to a point in the vector space, introducing only the amount of approximation inherent to the use of graph matching algorithms. In order to evaluate the proposed method, we compare it with the set median and with the other state-of-the-art embedding-based methods for the median graph computation. The experiments are carried out using four different databases (one semi-artificial and three containing real-world data). Results show that with the proposed approach we can obtain better medians, in terms of the sum of distances to the training graphs, than with the previous existing methods.

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An Error-Tolerant Approximate Matching Algorithm for Attributed Planar Graphs and Its Application to Fingerprint Classification

Cited by 59 publications

References 21 publications

Graph Matching Algorithms for Business Process Model Similarity Search

Graph Matching Algorithms for Business Process Model Similarity Search

Merging Business Process Models

A generic framework for median graph computation based on a recursive embedding approach

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