Related papers: Shape Statistics of Sloan Digital Survey superclus…
Although coherent large-scale structures such as filaments and walls are apparent to the eye in galaxy redshift surveys, they have so far proven difficult to characterize with computer algorithms. This paper presents a procedure that uses…
Time-series classification is an important problem for the data mining community due to the wide range of application domains involving time-series data. A recent paradigm, called shapelets, represents patterns that are highly predictive…
Understanding the clustering of galaxies has long been a goal of modern observational cosmology. Utilizing our photometric redshift technique a volume limited sample containing more than 2 million galaxies is constructed from the SDSS…
We study topological properties of large scale structure in a set of scale free N-body simulations using the genus and percolation curves as topological characteristics. Our results show that as gravitational clustering advances, the…
We present a study on galaxy detection and shape classification using topometric clustering algorithms. We first use the DBSCAN algorithm to extract, from CCD frames, groups of adjacent pixels with significant fluxes and we then apply the…
We present the results of a redshift survey of both cluster and intercluster galaxies in the central part of the Shapley Concentration, the richest supercluster of clusters in the nearby Universe, consisting of ~2000 radial velocities. We…
We present a study of morphologies, based on deep $u^{*}g^{\prime}i^{\prime}z^{\prime}$ imaging of the Virgo Cluster from the Next Generation Virgo Cluster Survey (NGVS), for 3689 Virgo cluster members spanning a mass range of…
We present a new, objectively defined catalog of candidate galaxy clusters based on the galaxy catalogs from the Digitized Second Palomar Observatory Sky Survey (DPOSS). This cluster catalog, derived from the best calibrated plates in the…
Clustering algorithms partition a dataset into groups of similar points. The primary contribution of this article is the Multiscale Spatially-Regularized Diffusion Learning (M-SRDL) clustering algorithm, which uses spatially-regularized…
Superclusters of galaxies mark the large-scale overdense regions in the Universe. Superclusters provide an ideal environment to study structure formation and to search for the emission of the intergalactic medium such as cosmic filaments…
Galaxies which fall into clusters as part of the same infall halo can retain correlations due to their shared origin. N-body simulations are used to study properties of such galaxy subgroups within clusters, including their richnesses and…
Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering. Existing time series clustering methods may fail to capture representative shapelets because they discover shapelets from…
With our previous study, the Super-k algorithm, we have introduced a novel way of piecewise-linear classification. While working on the Super-k algorithm, we have found that there is a similar, and simpler way to explain for obtaining a…
I review here past and present research on clusters and groups of galaxies within the Sloan Digital Sky Survey (SDSS). In particular, I discuss the C4 algorithm which is designed to search for clusters within a 7-dimensional data-space,…
The classification of galaxy morphology is a hot issue in astronomical research. Although significant progress has been made in the last decade in classifying galaxy morphology using deep learning technology, there are still some…
The large--scale structure (LSS) in the Universe comprises a complicated filamentary network of matter. We study this network using a high--resolution simulation of structure formation of a $\Lambda$ Cold Dark Matter cosmology. We…
Identifying possible clusters in datasets and estimating their overall modularity are central tasks in pattern recognition. In the present work, concepts and methodologies are described for performing these tasks while considering only the…
We analyze the morphological evolution of open clusters and provide shape parameters for 265 open clusters. The results show that the overall shape of sample clusters becomes more elliptical as they grow older, while their core remains…
In this paper, we address an issue of finding explainable clusters of class-uniform data in labelled datasets. The issue falls into the domain of interpretable supervised clustering. Unlike traditional clustering, supervised clustering aims…
According to the current cosmological paradigm, large scale structures form hierarchically in the Universe. Clusters of galaxies grow through a continuous accretion of mass. Nevertheless, the rate and manner of mass accretion events are…