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Classical approaches in cluster analysis are typically based on a feature space analysis. However, many applications lead to datasets with additional spatial information and a ground truth with spatially coherent classes, which will not…
Many small moonlets, creating propeller structures, have been found in Saturn's rings by the Cassini spacecraft. We study the dynamical evolution of such 20-50m sized bodies which are embedded in Saturn's rings. We estimate the importance…
The problem of organizing data that evolves over time into clusters is encountered in a number of practical settings. We introduce evolutionary subspace clustering, a method whose objective is to cluster a collection of evolving data points…
Micro-panel data are collected and analysed in many research and industry areas. Cluster analysis of micro-panel data is an unsupervised learning exploratory method identifying subgroup clusters in a data set which include homogeneous…
The Small Body Dynamics Tool (SBDynT) is software written for the community of Solar System small body researchers to perform dynamical classification, characterization, and investigation. SBDynT provides advanced simulation analysis…
This document aims at reviewing the different types of clustering algorithms and substructures detection techniques in order to study the spatial and kinematic clustering of stars and detect the gas components in molecular clouds. It is the…
There is a growing interest in characterizing circular data found in biological systems. Such data are wide ranging and varied, from signal phase in neural recordings to nucleotide sequences in round genomes. Traditional clustering…
Spatial cluster analysis, the detection of localized patterns of similarity in geospatial data, has a wide-range of applications for scientific discovery and practical decision making. One way to detect spatial clusters is by using local…
Algebraic Subspace Clustering (ASC) is a simple and elegant method based on polynomial fitting and differentiation for clustering noiseless data drawn from an arbitrary union of subspaces. In practice, however, ASC is limited to…
Knowledge of the optical turbulence profile is important in adaptive optics (AO) systems, particularly tomographic AO systems such as those to be employed by the next generation of 40 m class extremely large telescopes (ELTs). Site…
Modern online mass spectrometry generates multi-terabyte data streams critical for understanding Earth's environmental systems. However, extracting actionable chemical insights from these repositories is impeded by a computational…
The ACS Survey of Globular Clusters has used HST's Wide-Field Channel to obtain uniform imaging of 65 of the nearest globular clusters to provide an extensive homogeneous dataset for a broad range of scientific investigations. The survey…
We present an efficient and robust approach for extracting clusters of galaxies from weak lensing survey data and measuring their properties. We use simple, physically-motivated cluster models appropriate for such sparse, noisy data, and…
Robust measurements of cosmological parameters from galaxy surveys rely on our understanding of systematic effects that impact the observed galaxy density field. In this paper we present, validate, and implement the idea of adopting the…
Spatial clustering is a crucial field, finding universal use across criminology, pathology, and urban planning. However, most spatial clustering algorithms cannot pull information from nearby nodes and suffer performance drops when dealing…
We outline here the next generation of cluster-finding algorithms. We show how advances in Computer Science and Statistics have helped develop robust, fast algorithms for finding clusters of galaxies in large multi-dimensional astronomical…
Clustering is an essential data mining tool for analyzing and grouping similar objects. In big data applications, however, many clustering algorithms are infeasible due to their high memory requirements and/or unfavorable runtime…
Clustering analysis has received considerable attention in spatial data mining for several years. With the rapid development of the geospatial information technologies, the size of spatial information data is growing exponentially which…
Traditional studies of stellar clusters in external galaxies use surface photometry and therefore focus on systems that are still bright and compact enough to be separated from the stellar background. Consequently, the latter stages of…
The evaluation of clustering results is difficult, highly dependent on the evaluated data set and the perspective of the beholder. There are many different clustering quality measures, which try to provide a general measure to validate…