Self-organizing Graph Edit Distance
نویسندگان
چکیده
This paper addresses the issue of learning graph edit distance cost functions for numerically labeled graphs from a corpus of sample graphs. We propose a system of self-organizing maps representing attribute distance spaces that encode edit operation costs. The selforganizing maps are iteratively adapted to minimize the edit distance of those graphs that are required to be similar. To demonstrate the learning effect, the distance model is applied to graphs representing line drawings and diatoms.
منابع مشابه
Semantic Correspondence of Database Schema from Heterogeneous Databases using Self-Organizing Map
This paper provides a framework for semantic correspondence of heterogeneous databases using selforganizing map. It solves the problem of overlapping between different databases due to their different schemas. Clustering technique using self-organizing maps (SOM) is tested and evaluated to assess its performance when using different kinds of data. Preprocessing of database is performed prior to...
متن کاملMicrosoft Word - IROS09-final.docx
In our previous works we had developed a framework for self-reconfiguration planning based on graph signature and graph edit-distance. The graph signature is a fast isomorphism test between different configurations and the graph edit-distance is a similarity metric. But the algorithm is not suitable for modules with symmetry. In this paper we improve the algorithm in order to deal with symmetri...
متن کاملAircraft Engine Fleet Monitoring Using Self-Organizing Maps and Edit Distance
Aircraft engines are designed to be used during several tens of years. Ensuring a proper operation of engines over their lifetime is therefore an important and difficult task. The maintenance can be improved if efficient procedures for the understanding of data flows produced by sensors for monitoring purposes are implemented. This paper details such a procedure aiming at visualizing in a meani...
متن کاملImproved Interpretability of the Unified Distance Matrix with Connected Components
Self-organizing maps have been adopted in many fields as the data visualization method of choice. The unified distance matrix is the de facto standard for evaluating and interpreting self-organizing maps. In large, high-dimensional problems clusters can be difficult to identify in the plain unified distance matrix. Here we introduce an enhanced version of the unified distance matrix in which cl...
متن کاملEdit Distance From Graph Spectra
This paper is concerned with computing graph edit distance. One of the criticisms that can be leveled at existing methods for computing graph edit distance is that it lacks the formality and rigour of the computation of string edit distance. Hence, our aim is to convert graphs to string sequences so that standard string edit distance techniques can be used. To do this we use graph spectral seri...
متن کامل