While anomaly detection is relatively well-studied, it remains a topic of ongoing interest and challenge, as our society becomes increasingly interconnected and digitalized. In this paper, we focus on existing anomaly detection approaches, by empirically studying the performance of 29 semi-supervised anomaly detection algorithms on 95 benchmark imbalanced databases from the KEEL repository. These include well-established and commonly used classifiers (e.g., One-Class Support Vector Machine (ocSVM) and Isolation Forest) and recent proposals (e.g., BRM and XGBOD). Findings from our in-depth empirical study show that BRM is a robust classifier, in terms of achieving better classification results than the other 28 state-of-the-art techniques on diverse anomaly detection problems. We also observe that OCKRA, Isolation Forest, and ocSVM achieve good performance overall AUC, but poor classification results on databases where the number of objects is equal or greater than 1,460, all features are nominal, or the imbalance ratio is equal or greater than 39.14.

Semi-supervised anomaly detection algorithms: A comparative summary and future research directions

A survey of outlier detection in high dimensional data streams

Generally, extracting only expected knowledge from data is not sufficient since unexpected ones can hide useful information concerning the data behavior. These information can be further used to optimize the current state. This has lead to the outlier detection. It refers to the data mining task that aims to find abnormal points or sequence of data hidden in the dataset. In fact, due to the emergence of new technologies, applications often generate and consume data in form of streams. This data differs from the static one. Therefore, traditional techniques cannot be used. Hence, convenient ones suitable to the data stream nature must be applied. In this paper, we will review different techniques of outlier detection in the data streams. In addition, we shall describe different approaches based on these techniques in order to establish a comparative study based on different criterion. This study aims to help users and facilitates the choice of the appropriate algorithm for a certain context.

A Survey on Outlier Detection in the Context of Stream Mining: Review of Existing Approaches and Recommadations

Outlier detection is critical in many business applications, as it recognizes unusual behaviours to prevent losses and optimize revenue. For example, illegitimate online transactions can be detected based on its pattern with outlier detection. The performance of existing outlier detection methods is limited by the pattern/behaviour of the dataset; these methods may not perform well without prior knowledge of the dataset. This paper proposes a multi-level outlier detection algorithm (MCOD) that uses multi-level unsupervised learning to cluster the data and discover outliers. The proposed detection method is tested on datasets in different fields with different sizes and dimensions. Experimental analysis has shown that the proposed MCOD algorithm has the ability to improving the outlier detection rate, as compared to the traditional anomaly detection methods. Enterprises and organizations can adopt the proposed MCOD algorithm to ensure a sustainable and efficient detection of frauds/outliers to increase profitability (and/or) to enhance business outcomes.

https://www.mdpi.com/2504-2289/4/4/24/pdf

Multi-Level Clustering-Based Outlier’s Detection (MCOD) Using Self-Organizing Maps

This chapter provides an overview of the outlier detection problem and brings out various research issues connected with this problem. It presents a detailed survey of the available literature on this problem with respect to the research issues identified. Basically, this chapter sets the direction for the material presented in the rest of the chapters of this book.

Research Issues in Outlier Detection

Outlier detection over data streams is an increasingly important task in data mining. Traditional distance-based data stream outlier detection is unsuitable for high-dimensional data sets, since the discrimination of distances between different data points becomes rather poor in high dimensional space. ABOD (Angle-based Outlier Detection) is an effective approach to detecting outliers in high-dimensional space. In this paper, the problem of continuous ABOD over data streams is studied. Generally, only a few data objects may change their states during two consecutive timestamps. Therefore, we propose several incremental angle-based outlier detection approaches over data streams based on ABOD and its variants that provide visible speed-up without loss of accuracy. Firstly, the basic ideas of these incremental algorithms are introduced. Then, we explain the time complexity of them. Finally, we use synthetic data streams to prove their efficiency.

Continuous Angle-based Outlier Detection on High-dimensional Data Streams

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Continuous Angle-based Outlier Detection on High-dimensional Data Streams