title: Topology-based hierarchical clustering of self-organizing maps.
abstract: A powerful method in the analysis of datasets where there are many natural clusters with varying statistics such as different sizes, shapes, density distribution, overlaps, etc., is the use of self-organizing maps (SOMs). However, further processing tools, such as visualization and interactive clustering, are often necessary to capture the clusters from the learned SOM knowledge. A recent visualization scheme (CONNvis) and its interactive clustering utilize the data topology for SOM knowledge representation by using a connectivity matrix (a weighted Delaunay graph), CONN. In this paper, we propose an automated clustering method for SOMs, which is a hierarchical agglomerative clustering of CONN. We determine the number of clusters either by using cluster validity indices or by prior knowledge on the datasets. We show that, for the datasets used in this paper, data-topology-based hierarchical clustering can produce better partitioning than hierarchical clustering based solely on distance information.
authors.affiliation: European Commission Joint Research Centre, Institute for Environment and Sustainability, Monitoring Agricultural Resources Unit, Ispra 21027, Italy. kadim.tasdemir@jrc.ec.europa.eu
authors.forename: Kadim
authors.initials: K
authors.lastname: Taşdemir
authors.forename: Pavel
authors.initials: P
authors.lastname: Milenov
authors.forename: Brooke
authors.initials: B
authors.lastname: Tapsall
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008377:Maps as Topic; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis / Q000379:methods