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A Fast Greedy Algorithm for Outlier Mining

Zengyou He·Xiaofei Xu·Shengchun Deng
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Abstract

The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. In [38], the problem of outlier detection in categorical data is defined as an optimization problem and a local-search heuristic based algorithm (LSA) is presented. However, as is the case with most iterative type algorithms, the LSA algorithm is still very time-consuming on very large datasets. In this paper, we present a very fast greedy algorithm for mining outliers under the same optimization model. Experimental results on real datasets and large synthetic datasets show that: (1) Our algorithm has comparable performance with respect to those state-of-art outlier detection algorithms on identifying true outliers and (2) Our algorithm can be an order of magnitude faster than LSA algorithm.

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Zengyou He, Xiaofei Xu, Shengchun Deng. 2005-07-27. A Fast Greedy Algorithm for Outlier Mining. https://arxiv.org/abs/cs/0507065

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