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Outlier detection algorithms based on k nearest neighbors ( kNN) can effectively find outliers from massive data, but most algorithms are difficult to adapt to high-dimensional data sets. In order to highlight the importance of attributes in k nearest neighbors, we propose a weighted kNN query method, which uses the Z-order curve to find the kNN. The method first applies information entropy to calculate each attribute weight, and then uses the Z-order curve to encode high-dimensional data into Z-value. The weighted kNN of each object are searched according to its Z-value. Meanwhile, a novel outlier detection algorithm is presented based on the minimum distance and average distance between each object and its weighted kNN. On this basis, we propose a parallel outlier detection algorithm called POD to improve the efficiency of the outlier detection. Finally, we implement and evaluate POD algorithm on a 10-nodes Hadoop cluster, on which synthetic and UCI standard data are tested. Experimental results show that POD achieves high performance in terms of effectiveness, scalability and extensibility.

POD: A Parallel Outlier Detection Algorithm Using Weighted kNN