A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Median Graphs: Properties, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Efficient Regular Data Structures and Algorithms for Location and Proximity Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Central Clustering of Attributed Graphs
Machine Learning
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Algorithms for the Sample Mean of Graphs
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
The Journal of Machine Learning Research
Pattern Recognition & Matlab Intro
Pattern Recognition & Matlab Intro
Report on the second symbol recognition contest
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
On the relation between the common labelling and the median graph
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Cross-modal alignment for wildlife recognition
Proceedings of the 2nd ACM international workshop on Multimedia analysis for ecological data
| Hi-index | 0.00 |
This paper proposes a fast k-means algorithm for graphs based on Elkan's k-means for vectors. To accelerate the k-means algorithm for graphs without trading computational time against solution quality, we avoid unnecessary graph distance calculations by exploiting the triangle inequality of the underlying distance metric. In experiments we show that the accelerated k-means for graphs is faster than k-means for graphs provided there is a cluster structure in the data.