A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data

TL;DR

This paper introduces a novel local distance-based outlier detection method called LDOF, designed to effectively identify outliers in scattered real-world datasets by analyzing local relative distances.

Contribution

The paper proposes LDOF, a new outlier detection measure that addresses data scattering and parameter setting issues, with theoretical analysis and improved detection performance.

Findings

LDOF effectively detects outliers in scattered datasets.

LDOF's performance is stable over a wide parameter range.

LDOF outperforms traditional methods like top-n KNN and LOF.

Abstract

Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection methods are ineffective on scattered real-world datasets due to implicit data patterns and parameter setting issues. We define a novel "Local Distance-based Outlier Factor" (LDOF) to measure the {outlier-ness} of objects in scattered datasets which addresses these issues. LDOF uses the relative location of an object to its neighbours to determine the degree to which the object deviates from its neighbourhood. Properties of LDOF are theoretically analysed including LDOF's lower bound and its false-detection probability, as well as parameter settings. In order to facilitate parameter settings in real-world applications, we employ a top-n technique in our outlier detection approach, where only the objects with the highest LDOF values are regarded as outliers. Compared to conventional approaches (such as top-n KNN and top-n LOF), our method top-n LDOF is more effective at detecting outliers in scattered data. It is also easier to set parameters, since its performance is relatively stable over a large range of parameter values, as illustrated by experimental results on both real-world and synthetic datasets.