A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

The term "outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework

Ensemble Methods for Anomaly Detection

Anomaly detection has many applications in numerous areas such as intrusion detection, fraud detection, and medical diagnosis. Most current techniques are specialized for detecting one type of anomaly, and work well on specific domains and when the data satisfies specific assumptions. We address this problem, proposing ensemble anomaly detection techniques that perform well in many applications, with four major contributions: using bootstrapping to better detect anomalies on multiple subsamples, sequential application of diverse detection algorithms, a novel adaptive sampling and learning algorithm in which the anomalies are iteratively examined, and improving the random forest algorithms for detecting anomalies in streaming data. We design and evaluate multiple ensemble strategies using score normalization, rank aggregation and majority voting, to combine the results from six well-known base algorithms. We propose a bootstrapping algorithm in which anomalies are evaluated from multiple subsets of the data. Results show that our independent ensemble performs better than the base algorithms, and using bootstrapping achieves competitive quality and faster runtime compared with existing works. We develop new sequential ensemble algorithms in which the second algorithm performs anomaly detection based on the first algorithm\u27s outputs; best results are obtained by combining algorithms that are substantially different. We propose a novel adaptive sampling algorithm which uses the score output of the base algorithm to determine the hard-to-detect examples, and iteratively resamples more points from such examples in a complete unsupervised context. On streaming datasets, we analyze the impact of parameters used in random trees, and propose new algorithms that work well with high-dimensional data, improving performance without increasing the number of trees or their heights. We show that further improvements can be obtained with an Evolutionary Algorithm