Abstract
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.
Highlights
Illegal actions in business usually lead to a significant amount of financial loss, especially with those organizations that handle a large amount of data or metrics
The machine learning methods they used were k-nearest neighbour (KNN), random forest, and support vector machines (SVM), while the deep learning methods were convolutional neural networks (CNN), restricted Boltzmann machine (RBM), and deep belief networks (DBN)
The MCBOD algorithm relies on the fact that the clustering method in the first layer can provide the knowledge about the dataset as the initial seeds to the second layer to achieve better detection of outliers in the dataset
Summary
Illegal actions in business usually lead to a significant amount of financial loss, especially with those organizations that handle a large amount of data or metrics. The distance-based outlier detection method assumes inliers are closed to each other, and the density-based detection method believes inliers have more neighbour data points than outliners Those assumptions may not be suitable for general datasets with various types, sparsity, configurations, or prior labelling. The proposed MCOD algorithm uses self-organizing maps (SOM) [9] as the base level of clustering, due to its efficiency in handling several types of classification problems while providing a useful, interactive, and intelligible summary of the data. The rest of this paper is organized as follows: Section 2 introduces the background of current outlier detection methods; Section 3 presents the proposed model of multi-level clustering-based outlier detection; Section 4 describes the experimental analysis; Section 5 outlines the conclusions and the future works
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