---
title: A Fast Greedy Algorithm for Outlier Mining
url: https://www.emergentmind.com/papers/0507065
type: paper
arxiv_id: '0507065'
arxiv_url: https://arxiv.org/abs/0507065
published: '2005-07-27'
categories:
- cs.DB
- cs.AI
---

# A Fast Greedy Algorithm for Outlier Mining

## 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.