CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2006 vol.28 Issue No.11 - November
Issue No.11 - November (2006 vol.28)
P. Franti , Dept. of Comput. Sci., Joensuu Univ.
O. Virmajoki , Dept. of Comput. Sci., Joensuu Univ.
V. Hautamaki , Dept. of Comput. Sci., Joensuu Univ.
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(tauN log N) at the cost of a slight increase in distortion; here, tau denotes the lumber 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
Nearest neighbor searches, Clustering algorithms, Clustering methods, Tree graphs, Costs, Vector quantization, Buildings, Mean square error methods, Merging,PNN., Clustering, agglomeration, nearest neighbor, vector quantization
P. Franti, O. Virmajoki, V. Hautamaki, "Fast Agglomerative Clustering Using a k-Nearest Neighbor Graph", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.28, no. 11, pp. 1875-1881, November 2006, doi:10.1109/TPAMI.2006.227