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Abstract: KNN (k nearest neighbor) is widely discussed and applied in pattern recognition and data mining, however, as a similar outlier detection method using local information for mining a new outlier, neighborhood outlier detection, few literatures are reported on. In this paper, we introduce neighborhood model as a uniform framework to understand and implement neighborhood outlier detection. Furthermore, a neighborhood-based outlier detection algorithm is also given. This algorithm integrates rough set granular technique with outlier detecting. We propose a neighborhood-based metric on outlier detection, and compare neighborhood outlier detection with DIS, KNN and RNN. The experimental results show that neighborhood-based metric is able to measure the local information for outlier detection. The detected accuracies based on neighborhood outlier detection are superior to DIS, KNN for mixed dataset, and a litter better than RNN for discrete dataset. [ABSTRACT FROM AUTHOR]

Abstract: It is desirable to find unusual data objects by Ramaswamy et al.’s distance-based outlier definition, because only a metric distance function between two objects is required. This definition does not need any neighborhood distance threshold required by many existing algorithms based on the definition of Knorr and Ng. Bay and Schwabacher proposed an efficient algorithm ORCA, which can give near linear time performance, for this task. To further reduce the running time, we propose in this paper two algorithms RC and RS using the following two techniques, respectively: (i) faster cutoff update, and (ii) space utilization after pruning. We tested RC, RS, and RCS (a hybrid approach combining both RC and RS) on several large and high-dimensional real data sets with millions of objects. The experiments show that the speed of RCS is as fast as 1.4–2.3 times that of ORCA, and the improvement of RCS is relatively insensitive to the increase in the data size. [Copyright &y& Elsevier]