Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Self-organizing maps
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
Error Correcting Graph Matching: On the Influence of the Underlying Cost Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
The String-to-String Correction Problem
Journal of the ACM (JACM)
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
ACM Computing Surveys (CSUR)
Mean and maximum common subgraph of two graphs
Pattern Recognition Letters
Graph distances using graph union
Pattern Recognition Letters
A graph distance metric combining maximum common subgraph and minimum common supergraph
Pattern Recognition Letters
Principles of data mining
On Median Graphs: Properties, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Machine Learning
Weighted mean of a pair of graphs
Computing
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Self-organizing map for clustering in the graph domain
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Marked Subgraph Isomorphism of Ordered Graphs
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Spectral Feature Vectors for Graph Clustering
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Validation indices for graph clustering
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Linear time algorithm for isomorphism of planar graphs (Preliminary Report)
STOC '74 Proceedings of the sixth annual ACM symposium on Theory of computing
Classification of Web Documents Using a Graph Model
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
On graphs with unique node labels
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Self-organizing graph edit distance
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Graph edit distance with node splitting and merging, and its application to diatom identification
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Theoretical analysis and experimental comparison of graph matching algorithms for database filtering
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Comparison of two different prediction schemes for the analysis of time series of graphs
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Analysis of time series of graphs: prediction of node presence by means of decision tree learning
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Inferring the semantic properties of sentences by mining syntactic parse trees
Data & Knowledge Engineering
Machine learning of syntactic parse trees for search and classification of text
Engineering Applications of Artificial Intelligence
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Many powerful methods for intelligent data analysis have become available in the fields of machine learning and data mining. However, almost all of these methods are based on the assumption that the objects under consideration are represented in terms of feature vectors, or collections of attribute values. In the present paper we argue that symbolic representations, such as strings, trees or graphs, have a representational power that is significantly higher than the representational power of feature vectors. On the other hand, operations on these data structure that are typically needed in data mining and machine learning are more involved than their counterparts on feature vectors. However, recent progress in graph matching and related areas has led to many new practical methods that seem to be very promising for a wide range of applications.