Comparison of Outlier Detection Algorithms on String Data

TL;DR

This paper compares two novel algorithms for detecting outliers in string data, one based on a modified local outlier factor using Levenshtein distance and another on hierarchical regular expressions, demonstrating their effectiveness in different scenarios.

Contribution

Introduces a variant of local outlier factor tailored for string data and a new hierarchical regular expression-based outlier detection algorithm, with experimental comparison.

Findings

Regular expression-based method excels with structurally distinct outliers.

Levenshtein-based LOF performs well when outliers have different edit distances.

Both algorithms can conceptually identify string data outliers.

Abstract

Outlier detection is a well-researched and crucial problem in machine learning. However, there is little research on string data outlier detection, as most literature focuses on outlier detection of numerical data. A robust string data outlier detection algorithm could assist with data cleaning or anomaly detection in system log files. In this thesis, we compare two string outlier detection algorithms. Firstly, we introduce a variant of the well-known local outlier factor algorithm, which we tailor to detect outliers on string data using the Levenshtein measure to calculate the density of the dataset. We present a differently weighted Levenshtein measure, which considers hierarchical character classes and can be used to tune the algorithm to a specific string dataset. Secondly, we introduce a new kind of outlier detection algorithm based on the hierarchical left regular expression learner, which infers a regular expression for the expected data. Using various datasets and parameters, we experimentally show that both algorithms can conceptually find outliers in string data. We show that the regular expression-based algorithm is especially good at finding outliers if the expected values have a distinct structure that is sufficiently different from the structure of the outliers. In contrast, the local outlier factor algorithms are best at finding outliers if their edit distance to the expected data is sufficiently distinct from the edit distance between the expected data.