Outlier Detection Using Inductive Logic Programming

2009 Ninth IEEE International Conference on Data Mining Pub Date : 2009-12-06 DOI:10.1109/ICDM.2009.127

F. Angiulli, Fabio Fassetti

引用次数: 7

Abstract

We present a novel definition of outlier in the context of inductive logic programming. Given a set of positive and negative examples, the definition aims at singling out the examples showing anomalous behavior. We note that the task here pursued is different from noise removal, and, in fact, the anomalous observations we discover are different in nature from noisy ones. We discuss pecularities of the novel approach, present an algorithm for detecting outliers, discuss some examples of knowledge mined, and compare it with alternative approaches.

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用归纳逻辑编程进行离群值检测

在归纳逻辑规划的背景下，提出了离群值的新定义。给定一组积极和消极的例子，该定义旨在挑出表现出异常行为的例子。我们注意到，这里所追求的任务不同于噪声去除，事实上，我们发现的异常观测在性质上不同于噪声观测。我们讨论了新方法的特点，提出了一种检测异常值的算法，讨论了一些知识挖掘的例子，并将其与其他方法进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2009 Ninth IEEE International Conference on Data Mining

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