Bioarchaeology aims to reconstruct, e.g., the diet or provenance of animals and humans in archaeological times. This can be done by investigating so-called multi-isotope fingerprints, resulting from the analysis of several different isotope systems in parallel. However, the multi-isotope fingerprint of samples from coastal regions can be influenced by the so-called sea spray effect, resulting in "too marine" isotope signatures in terrestrial herbivorous individuals, falling in-between less or un-affected herbivores and marine mammals when clustering the isotope data ("sea spray cluster"), what cannot be explained by the diet or habitat of these individuals. The recently proposed in-between instance (IBI) definition allows the detection of additional sea spray candidates, not grouped into the sea spray cluster, thus less affected by sea spray but still of interest to domain experts. The sea spray effect locally expected in archaeological individuals can also be investigated by isotope analysis in modern plants of the same region. The fusion of isotope data measured in archaeological bones and in modern plants allows us to investigate samples (un-)affected by sea spray, what is relevant for domain experts to understand the potential local range of isotope values at an archaeological site.

Abstract We discuss topological aspects of cluster analysis and show that inferring the topological structure of a dataset before clustering it can considerably enhance cluster detection: we show that clustering embedding vectors representing the inherent structure of a dataset instead of the observed feature vectors themselves is highly beneficial. To demonstrate, we combine manifold learning method UMAP for inferring the topological structure with density-based clustering method DBSCAN. Synthetic and real data results show that this both simplifies and improves clustering in a diverse set of low- and high-dimensional problems including clusters of varying density and/or entangled shapes. Our approach simplifies clustering because topological pre-processing consistently reduces parameter sensitivity of DBSCAN. Clustering the resulting embeddings with DBSCAN can then even outperform complex methods such as SPECTACL and ClusterGAN. Finally, our investigation suggests that the crucial issue in clustering does not appear to be the nominal dimension of the data or how many irrelevant features it contains, but rather how separable the clusters are in the ambient observation space they are embedded in, which is usually the (high-dimensional) Euclidean space defined by the features of the data. The approach is successful because it performs the cluster analysis after projecting the data into a more suitable space that is optimized for separability, in some sense.

Abstract Unsupervised learning methods are well established in the area of anomaly detection and achieve state of the art performances on outlier datasets. Outliers play a significant role, since they bear the potential to distort the predictions of a machine learning algorithm on a given dataset. Especially among PCA-based methods, outliers have an additional destructive potential regarding the result: they may not only distort the orientation and translation of the principal components, they also make it more complicated to detect outliers. To address this problem, we propose the robust outlier detection algorithm CoMadOut, which satisfies two required properties: (1) being robust towards outliers and (2) detecting them. Our CoMadOut outlier detection variants using comedian PCA define, dependent on its variant, an inlier region with a robust noise margin by measures of in-distribution (variant CMO) and optimized scores by measures of out-of-distribution (variants CMO*), e.g. kurtosis-weighting by CMO+k. These measures allow distribution based outlier scoring for each principal component, and thus, an appropriate alignment of the degree of outlierness between normal and abnormal instances. Experiments comparing CoMadOut with traditional, deep and other comparable robust outlier detection methods showed that the performance of the introduced CoMadOut approach is competitive to well established methods related to average precision (AP), area under the precision recall curve (AUPRC) and area under the receiver operating characteristic (AUROC) curve. In summary our approach can be seen as a robust alternative for outlier detection tasks.

Our research focuses on detecting and tracking ocean carbon regimes, which are crucial indicators for understanding the impacts of climate change on ocean carbon uptake. Geoscientific datasets in Earth System Sciences often contain local and distinct statistical distributions at a regional scale. This poses a significant challenge in applying conventional clustering algorithms for data analysis. Based on the observed limitations of prominent methods, in our study, we propose a framework that enhances well-established unsupervised machine-learning methods tailored to applications on geoscientific datasets. We define a carbon uptake regime as a region characterized by common relationships between the carbon uptake and its drivers, as simulated by a multi-annual hydrodynamic model simulation. As a first step, we compute multivariate linear regressions capturing local spatial relations between carbon dioxide uptake and its drivers to discover such regimes. This is followed by an agglomerative hierarchical clustering constructed upon the collection of regional multivariate linear regression models. To overcome the emerging limitations of a global cut for partitioning, which is inadequate to capture the local statistical distributions, we present a novel, straightforward and adaptive approach to detect and visualize ocean carbon uptake regimes in this work. This method relies on the distance-variance selection technique and detects multiple local cuts on the dendrogram by considering both the compactness and similarity of the clusters. Detecting meaningful and well-defined carbon uptake regimes is vital for their tracking over time. The tracking is performed through a simple yet effective approach where summary structures derived from the clusters are traced over time. Applied over longer time scales, this novel method will enable marine scientists to effectively monitor the impacts of climate change on the ocean carbon cycle more.

The increasing digital traces of fishing fleets nowadays available allow for automatized observation of the oceans, a vulnerable space which could hardly be monitored or governed previously. Data streams from satellite base communication systems are being used for a variety of applications such as collision avoidance, route optimization, and monitoring of illegal activities.

Our research focuses on the detection of ocean carbon uptake regimes that are critical in the context of comprehending climate change. One observation among geoscientific data in Earth System Sciences is that the datasets often contain local and distinct statistical distributions posing a major challenge in applying clustering algorithms for data analysis. The use of global parameters in many clustering algorithms is often inadequate to capture such local distributions. In this study, we propose a novel tool to detect and visualize oceanic carbon uptake clusters. We implement a distance-variance selection method (augmented by BIC scores) on agglomerative hierarchical clustering constructed upon a regional multivariate linear regression model set. Instead of relying on a global distance, users can select the local distance and variance thresholds on our tool to detect the connections on the dendrograms that stand as potential clusters by considering both compactness and similarity.

During different steps in the process of discovering drug candidates for diseases, it can be supportive to identify groups of molecules that share similar properties, i.e. common overall structural similarity. The existing methods for computing (dis)similarities between chemical structures rely on a priori domain knowledge. Here we investigate the clustering of compounds that are applied on embeddings generated from a recently published Mol2Vec technique which enables an entirely unsupervised vector representation of compounds. A research question we address in this work is: do existent well-known clustering algorithms such as k-means or hierarchical clustering methods yield meaningful clusters on the Mol2Vec embeddings? Further, we investigate how far subspace clustering can be utilized to compress the data by reducing the dimensionality of the compounds vector representation. Our first conducted experiments on a set of COVID-19 drug candidates reveal that well-established methods yield meaningful clusters. Preliminary results from subspace clusterings indicate that a compression of the vector representations seems viable.

Abstract OPTICS is a popular tool to analyze the clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-based clustering specified by the height as the density threshold for clusters. However, in very dynamic and rapidly changing applications, a complex and finely detailed visualization slows down the knowledge discovery. Instead, a framework that provides fast but coarse insights is required to point out structures in the data quickly. The user can then control the direction he wants to put emphasize on for refinement. We develop AMTICS as a novel and efficient divide-and-conquer approach to pre-cluster data in distributed instances and align the results in a hierarchy afterward. An interactive online phase ensures a low complexity while giving the user full control over the partial cluster instances. The offline phase reveals the current data clustering structure with low complexity and at any time.

LUCK allows to use any distance-based clustering algorithm to find linear correlated data. For that a novel distance function is introduced, which takes the distribution of the kNN of points into account and corresponds to the probability of two points being part of the same linear correlation. In this work in progress we tested the distance measure with DBSCAN and k-Means comparing it to the well-known linear correlation clustering algorithms ORCLUS, 4C, COPAC, LMCLUS, and CASH, receiving good results for difficult synthetic data sets containing crossing or non-continuous correlations.

In publications where a clustering method is described, the chosen hyperparameters are in many cases to our current observation empirically determined. In this work in progress we discuss and propose one approach on how hyperparameters can be systematically explored and their effects regarding the data set analyzed. We further introduce in the context of hyperparameter analysis a modified definition of the resilience term, which refers here to a subset of data points which persists to be in the same cluster over different hyperparameter settings. In order to analyze relations among different hyperparameters we further introduce the concept of dynamic intersection computing.

Periodicities are omnipresent: In nature in the cycles of predator and prey populations, reoccurring patterns regarding our power consumption over the days, or the presence of flu diseases over the year. With regards to the importance of periodicities we ask: Is there a way to detect periodic correlated clusters which are hidden in event series? We propose as a work in progress a method for detecting sinusoidal periodic correlated clusters on event series which relies on parameter space transformation. Our contributions are: Providing the first non-linear correlation clustering algorithm for detecting periodic correlated clusters. Further our method provides an explicit model giving domain experts information on parameters such as amplitude, frequency, phase-shift and vertical-shift of the detected clusters. Beyond that we approach the issue of determining an adequate frequency and phase-shift of the detected correlations given a frequency and phase-shift boundary.

Analyzing flyways of birds is one approach ornithologists pursue e.g. to be able to detect potential risks during the animal's migration. But this analysis is not trivial and the functionalities of existing supporting tools are neither perfect nor all-encompassing. In this paper, we introduce our new FATBIRD Tool, which not only visualizes flyways or arbitrary trajectories, but also helps the researchers in several aspects of the analysis. Similarities between all trajectories of the individual birds are calculated via Dynamic Time Warping distances, which is to the best of our knowledge the first usage in this field and delivers promising results. We show the functionalities of our tool on a use case based on real data of a GPS/GSM telemetry study of Eurasian curlews of the "Bavarian Society for the Protection of Birds". The similarities are shown in an intuitively understandable heat map colored distance matrix as well as a hierarchical clustering dendrogram. The clustering of all data points is performed and shown, and the data can be filtered by several parameters. With that, potential stop-over and wintering areas can be detected very fast and easily. After having obtained the similarities and differences of the trajectories in an automatic way, the researchers can focus on the biological reasons of the generated results of the FATBIRD Tool. These can lead to a better understanding of e.g. why certain birds die on their flyways and thus to new approaches to develop optimized conservation measures for the specific species.

Many algorithms have been developed for detecting clusters of various kinds over the past decades. However, just few attempts have been made to provide an interactive setting for the clustering algorithms. In this paper, we present PARADISO, an interactive Mean Shift method. It enables the user to get back to any arbitrary iteration point of the run observing the evolution of the clusters after each iteration and to set different bandwidth parameters. The user gets a clustering result with this method which emerged through multiple bandwidths while the user can see the full chain of effects of the chosen bandwidths over all iterations. Further, our method provides so-called Points-Shifted-Distance plots (PSD plots) for the Mean Shift algorithm which aim to facilitate the choice of a different bandwidth for the user. Beyond the mentioned features, PARADISO provides a visualization method which lets the user see the different bandwidth choices made in form of pathways.

Correlation clustering detects complex and intricate relationships in high-dimensional data by identifying groups of data points, each characterized by differents correlation among a (sub)set of features. Current correlation clustering methods generally limit themselves to linear correlations only. In this paper, we introduce a method for detecting global non-linear correlated clusters focusing on quadratic relations. We introduce a novel Hough transform for the detection of hyperparaboloids and apply it to the detection of hyperparaboloid correlated clusters in arbitrary high-dimensional data spaces. Non-linear correlation clustering like our method can reveal valuable insights which are not covered by current linear versions. Our empirical results on synthetic and real world data reveal that the proposed method is robust against noise, jitter and irregular densities.