Probabilistic K-nearest neighbor search in uncertain time-series database
In order to search the probabilistic K-nearest neighbor in uncertain time-series databases, this paper investigates dimension reduction and index pruning. The complexity of the high dimensionality and the uncertainty of uncertain time series is considered. Based on piecewise linear approximation (PLA),three lemmas are proposed to improve searching efficiency,which are no-dismissal pruning, the computation for probability of K-nearest neighbors and its upper limit. A probabilistic K-nearest neighbors search for uncertain time series ( PKNNS) algorithm is proposed to avoid dimensionality curse. Experimental results show the efficiency and effectiveness.
国家科技期刊平台
NSTL
万方数据
维普
