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Sharing data with third parties is essential for advancing science, but it is becoming more and more difficult with the rise of data protection regulations, ethical restrictions, and growing fear of misuse. Fully synthetic data, which transcends ...

Expressive evaluation metrics are indispensable for informative experiments in all areas, and while several metrics are established in some areas, in others, such as feature selection, only indirect or otherwise limited evaluation metrics are ...

Outlier detection algorithms typically assign an outlier score to each observation in a dataset, indicating the degree to which an observation is an outlier. However, these scores are often not comparable across algorithms and can be difficult for ...

Efficient and reliable methods for distinguishing outliers in data remain crucial for data analysis. Although supervised methods based on neural networks have gained recent traction, unsupervised methods such as the kNN outlier method and local ...

An outlier probability is the probability that an observation is an outlier. Typically, outlier detection algorithms calculate real-valued outlier scores to identify outliers. Converting outlier scores into outlier probabilities increases the ...

The detection of anomalies in streaming data faces complexities that make traditional static methods unsuitable due to computational costs and nonstationarity. We test and evaluate eight state of the art algorithms against prominent challenges ...

• Comparison and study of 8 unsupervised outlier detection methods for streaming data.
• Evaluation with 180 synthetic datasets and 4 real/application datasets.
• Study of challenges related to space geometries, nonstationarity and ...

Sparse Data Observers (SDO) is an unsupervised learning approach developed to cover the need for fast, highly interpretable and intuitively parameterizable anomaly detection. We present SDOclust, an extension that performs clustering while ...

It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem (Janssens and Postma, in: Proceedings of the 18th annual Belgian-Dutch on machine learning, pp 56–64, 2009; Janssens et al. in: ...

Embedding learning in multiplex networks has drawn increasing attention in recent years and achieved outstanding performance in many downstream tasks. However, most existing network embedding methods either only focus on the structured information of ...

Network embedding has recently attracted attention a lot since networks are widely used in various data mining applications. Attempting to break the limitations of pre-set meta-paths and non-global node learning in existing models, we propose a simple ...

The evaluation of unsupervised algorithm results is one of the most challenging tasks in data mining research. Where labeled data are not available, one has to use in practice the so-called internal evaluation, which is based solely on the data ...

It is well known that recall rather than precision is the performance measure to optimize in imbalanced classification problems, yet most existing methods that adjust for class imbalance do not particularly address the optimization of recall. Here ...

Semi-supervised classification methods are specialized to use a very limited amount of labelled data for training and ultimately for assigning labels to the vast majority of unlabelled data. Label propagation is such a technique that assigns ...

The article, A unified view of density-based methods for semi-supervised.

The application of clustering involves the interpretation of objects placed in multi-dimensional spaces. The task of clustering itself is inherently submitted to subjectivity, the <italic>optimal</italic> solution can be extremely costly to discover and ...

Although there is a large and growing literature that tackles the unsupervised outlier detection problem, the unsupervised evaluation of outlier detection results is still virtually untouched in the literature. The so-called internal evaluation, based ...

Semi-supervised learning is drawing increasing attention in the era of big data, as the gap between the abundance of cheap, automatically collected unlabeled data and the scarcity of labeled data that are laborious and expensive to obtain is ...

Axis-aligned subspace clustering generally entails searching through enormous numbers of subspaces (feature combinations) and evaluation of cluster quality within each subspace. In this paper, we tackle the problem of identifying subsets of ...

Data mining methods for outlier detection are usually based on non-parametric density estimates in various variations. Here we argue for the use of local intrinsic dimensionality as a measure of outlierness and demonstrate empirically that it is a ...