Algorithms for clustering data
Algorithms for clustering data
On the acquisition of object concepts from sensory data
Neural Computers
A deterministic annealing approach to clustering
Pattern Recognition Letters
Symbolic and Geometric Connectivity Graph Methods for Route Planning in Digitized Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering by discovery on maps
Pattern Recognition Letters
Dynamic clustering for time incremental data
Pattern Recognition Letters
Topological clustering of maps using a genetic algorithm
Pattern Recognition Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Clustering Algorithms
Map Learning and Clustering in Autonomous Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory of Sensor-Based Robot Navigation using Local Information
AI*IA Proceedings of the 2nd Congress of the Italian Association for Artificial Intelligence on Trends in Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Fingerprint Classification by Directional Image Partitioning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multihierarchical Graph Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Genetic Approach to Hierarchical Clustering of Euclidean Graphs
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
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The problem of organizing and exploiting spatial knowledge for navigation is an important issue in the field of autonomous mobile systems. In particular, partitioning the environment map into connected clusters allows for significant topological features to be captured and enables decomposition of path-planning tasks through a divide-and-conquer policy. Clustering by discovery is a procedure for identifying clusters in a map being learned by exploration as the agent moves within the environment, and yields a valid clustering of the available knowledge at each exploration step. In this work, we define a fitness measure for clustering and propose two incremental heuristic algorithms to maximize it. Both algorithms determine clusters dynamically according to a set of topological and metric criteria. The first one is aimed at locally minimizing a measure of "scattering" of the entities belonging to clusters, and partially rearranges the existing clusters at each exploration step. The second estimates the positions and dimensions of clusters according to a global map of density. The two algorithms are compared in terms of optimality, efficiency, robustness, and stability.