An efficient branch-and-bound nearest neighbour classifier
Pattern Recognition Letters
A deterministic annealing approach to clustering
Pattern Recognition Letters
Optimal circular fit to objects in two and three dimensions
Pattern Recognition Letters
Elliptic fit of objects in two and three dimensions by moment of inertia optimization
Pattern Recognition Letters
Biologically plausible models of place recognition and goal location
Parallel distributed processing
Complex transitive closure queries on a fragmented graph
ICDT '90 Proceedings of the third international conference on database theory on Database theory
Handbook of theoretical computer science (vol. A)
Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering by discovery on maps
Pattern Recognition Letters
Dynamic Clustering of Maps in Autonomous Agents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automomous Search in Complex Spaces
ECDL '98 Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries
Autonomous search for information in an unknown environment
CIA'99 Proceedings of the 3rd international conference on Cooperative information agents III
Pattern analysis with graphs: Parallel work at Bern and York
Pattern Recognition Letters
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Building autonomous systems, self-learning while moving in an unknown environment, finds a variety of challenging applications. This paper presents a new approach, called clustering by discovery, for identification of clusters in a map which is being learned by exploration. The concomitance of exploration and clustering, we argue, is a mandatory feature for an autonomous system, hence the clustering technique we propose is an incremental process performed while the system is learning the map. Clusters supply an abstract description of the environment and can be used to decrease the complexity of the navigational tasks. The environment is viewed as a map of distinctive places which we assume to be sensed and recognized by the system. The presence of distinctive places and the environment scale are the only facts which we assume known apriori to the system. Clustering by discovery is based on a heuristic indicator called scattering, whose increment is minimized at each exploration step compatibly with a connectivity constraint imposed on clusters. Scattering is defined according to a number of functional and structural requirements. Two variants are presented, and their performance is discussed on a sample of maps including a real urban map and some randomly generated ones. In particular, one of the variants shows robust behaviour in terms of independence of the exploration strategy adopted.