LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Enhancing Effectiveness of Outlier Detections for Low Density Patterns
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
DOLPHIN: An efficient algorithm for mining distance-based outliers in very large datasets
ACM Transactions on Knowledge Discovery from Data (TKDD)
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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In this paper, we propose a nearest neighbor based outlier detection algorithm, NDoT. We introduce a parameter termed as Nearest Neighbor Factor (NNF) to measure the degree of outlierness of a point with respect to its neighborhood. Unlike the previous outlier detection methods NDoT works by a voting mechanism. Voting mechanism binarizes the decision compared to the top-N style of algorithms. We evaluate our method experimentally and compare results of NDoT with a classical outlier detection method LOF and a recently proposed method LDOF. Experimental results demonstrate that N DoT outperforms LDOF and is comparable with LOF.