Issue No. 02 - February (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.232
Xiao-Tong Yuan , Rutgers University, Newark
Bao-Gang Hu , Institute of Automation Chinese Academy of Sciences, Beijing
Ran He , Institute of Automation Chinese Academy of Sciences, Beijing
Mean-Shift (MS) is a powerful nonparametric clustering method. Although good accuracy can be achieved, its computational cost is particularly expensive even on moderate data sets. In this paper, for the purpose of algorithmic speedup, we develop an agglomerative MS clustering method along with its performance analysis. Our method, namely Agglo-MS, is built upon an iterative query set compression mechanism which is motivated by the quadratic bounding optimization nature of MS algorithm. The whole framework can be efficiently implemented in linear running time complexity. We then extend Agglo-MS into an incremental version which performs comparably to its batch counterpart. The efficiency and accuracy of Agglo-MS are demonstrated by extensive comparing experiments on synthetic and real data sets.
Mean-shift, agglomerative clustering, half-quadratic optimization, incremental clustering.
B. Hu, X. Yuan and R. He, "Agglomerative Mean-Shift Clustering," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 209-219, 2010.