Improvement of Detection Algorithm Density-Based Local Outlier
For the high time complexity of the density-based outlier detecting algorithm (LOF algorithms), proposes an improved algorithm DBLOF with two-stage by optimizing the neighborhood query operation of adjacent objects for each data object. Firstly, clustering algorithm DB-SCAN is taken to preprocess the dataset and remove the most of the non-outliers data objects to get the dataset of all possible outliers. Then, the local outlier factors are calculated on the possible outliers dataset for each data object to find out the real outliers. The experi-ments demonstrate that the proposed algorithm can realize the effective local outlier detection and reduce the time complexity of the algo-rithm.
NETL
NSTL
万方数据
维普
