A Fast Greedy Algorithm for Outlier Mining
<|reference_start|>A Fast Greedy Algorithm for Outlier Mining: 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.<|reference_end|>
@article{he2005a,
    title={A Fast Greedy Algorithm for Outlier Mining},
    author={Zengyou He, Xiaofei Xu, Shengchun Deng},
    journal={arXiv preprint arXiv:cs/0507065},
    year={2005},
    number={Tr-05-0406},
    archivePrefix={arXiv},
    eprint={cs/0507065},
    primaryClass={cs.DB cs.AI}
}