title: Fast agglomerative clustering using a k-nearest neighbor graph.
abstract: We propose a fast agglomerative clustering method using an approximate nearest neighbor graph for reducing the number of distance calculations. The time complexity of the algorithm is improved from O(tauN2) to O(tauNlogN) at the cost of a slight increase in distortion; here, tau denotes the number of nearest neighbor updates required at each iteration. According to the experiments, a relatively small neighborhood size is sufficient to maintain the quality close to that of the full search.
authors.affiliation: Speech and Image Processing Unit, Department of Computer Science, University of Joensuu, Finland. franti@cs.joensuu.fi
authors.forename: Pasi
authors.initials: P
authors.lastname: Fränti
authors.forename: Olli
authors.initials: O
authors.lastname: Virmajoki
authors.forename: Ville
authors.initials: V
authors.lastname: Hautamäki
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*