Because outliers are very different from the rest of the data, it is natural to represent outliers by their distances to other objects. Furthermore, there are many scenarios in which only pairwise distances are known, and feature-based outlier detection methods cannot directly be applied. Considering these observations, and given the success of Isolation Forests for (feature-based) outlier detection, we propose Proximity Isolation Forest, a proximity-based extension. The methodology only requires a set of pairwise distances to work, making it suitable for different types of data. Analogously to Isolation Forest, outliers are detected via their early isolation in the trees; to encode the isolation we design nine training strategies, both random and optimized. We thoroughly evaluate the proposed approach on fifteen datasets, successfully assessing its robustness and suitability for the task; additionally we compare favourably to alternative proximity-based methods.

Arguably, a desirable feature of a learner is that its performance gets better with an increasing amount of training data, at least in expectation. This issue has received renewed attention in recent years and some curious and surprising findings have been reported on. In essence, these results show that more data does actually not necessarily lead to improved performance—worse even, performance can deteriorate. Clustering, however, has not been subjected to such kind of study up to now. This paper shows that k-means clustering, a ubiquitous technique in machine learning and data mining, suffers from the same lack of so-called monotonicity and can display deterioration in expected performance with increasing training set sizes. Our main, theoretical contributions prove that 1-means clustering is monotonic, while 2-means is not even weakly monotonic, i.e., the occurrence of nonmonotonic behavior persists indefinitely, beyond any training sample size. For larger k, the question remains open.

Isolation Forests are one of the most successful outlier detection techniques: they isolate outliers by performing random splits in each node. It has been recently shown that a trained Random Forest-based model can also be used to define and extract informative distance measures between objects. Although their success has been shown mainly in the clustering field, we propose to extract these pairwise distances between the objects from an Isolation Forest and use them as input to a distance or density-based outlier detector. We show that the extracted distances from Isolation Forests are able to describe outliers meaningfully. We evaluate our technique on ten benchmark datasets for outlier detection: we employ three different distance measures and evaluate the obtained representation using a density-based classifier, the Local Outlier Factor. We also compare the methodology to the standard Isolation Forests scheme.

We study the problem of Protein Remote Homology Detection, which assesses the functional similarity of two proteins. We approach this as a problem of binary multiple-instance learning (MIL) that aims to distinguish between homologous and non-homologous proteins. The particular MIL approach employed is based on the dissimilarity representation in which various schemes of combining N-gram representations are considered. This approach allows us to cope with longer N-grams, capturing a richer biological context, and results in versatile framework offering competitive performance compared to state of the art.