Related papers: Distant Cluster Search: Welcoming some Newcomers f…

Abridged] We present here new results on the space density of rich, optically-selected, clusters of galaxies at low redshift (z<0.15). These results are based on the application of the matched filter cluster-finding algorithm to 1067 deg2…

An atlas of deep, wide-field R-band CCD images of a complete sample of distant, X-ray-selected clusters of galaxies is presented. These clusters are the 41 most distant (z>= 0.15) and most X-ray luminous (Lx >= 2x10**44 erg\s-1) clusters in…

Clustering algorithms have long been the topic of research, representing the more popular side of unsupervised learning. Since clustering analysis is one of the best ways to find some clarity and structure within raw data, this paper…

I present the first results of a search for clusters of galaxies in Chandra ACIS pointed observations at high galactic latitude with exposure times larger than 10 ks. The survey is being carried out using the Voronoi Tessellation and…

Data from the Gaia satellite are revolutionising our understanding of the Milky Way. With every new data release, there is a need to update the census of open clusters. We aim to conduct a blind, all-sky search for open clusters using 729…

We have conducted an automated search for galaxy clusters within a contiguous 16 square degree I-band survey in the north Galactic hemisphere. A matched filter detection algorithm identifies 444 cluster candidates in the range 0.2 <= z <=…

We present a clustering method and provide a theoretical analysis and an explanation to a phenomenon encountered in the applied statistical literature since the 1990's. This phenomenon is the natural adaptability of the order when using a…

In recent years, large-scale Bayesian learning draws a great deal of attention. However, in big-data era, the amount of data we face is growing much faster than our ability to deal with it. Fortunately, it is observed that large-scale…

A least squares semi-supervised local clustering algorithm based on the idea of compressed sensing is proposed to extract clusters from a graph with known adjacency matrix. The algorithm is based on a two-stage approach similar to the one…

Many algorithms for approximate nearest neighbor search in high-dimensional spaces partition the data into clusters. At query time, in order to avoid exhaustive search, an index selects the few (or a single) clusters nearest to the query…

Searches for continuous gravitational waves target nearly monochromatic gravitational wave emission from e.g. non-axysmmetric fast-spinning neutron stars. Broad surveys often require to explicitly search for a very large number of different…

We present a new fast online clustering algorithm that reliably recovers arbitrary-shaped data clusters in high throughout data streams. Unlike the existing state-of-the-art online clustering methods based on k-means or k-medoid, it does…

This paper studies the fair range clustering problem in which the data points are from different demographic groups and the goal is to pick $k$ centers with the minimum clustering cost such that each group is at least minimally represented…

We present spectroscopic observations of galaxies in 4 clusters at z = 0.7-0.8 and in one cluster at z~0.5 obtained with the FORS2 spectrograph on the VLT as part of the ESO Distant Cluster Survey (EDisCS), a photometric and spectroscopic…

Context: Galaxy clusters at high redshift are important to test cosmological models and models for the growth of structure. They are difficult to find in wide-angle optical surveys, however, leaving dedicated follow-up of X-ray selected…

Given a point set S and an unknown metric d on S, we study the problem of efficiently partitioning S into k clusters while querying few distances between the points. In our model we assume that we have access to one versus all queries that…

We describe the design and current status of a new X-ray cluster survey aimed at the compilation of a statistically complete sample of very X-ray luminous (and thus, by inference, massive), distant clusters of galaxies. The primary goal of…

We show how one may expect a significant number of SZ detections in future Planck data without any of the typical assumptions needed in present component separation methods, such as about the power spectrum or the frequency dependence of…

We propose a new model-independent method for new physics searches called Cluster Scanning. It uses the k-means algorithm to perform clustering in the space of low-level event or jet observables, and separates potentially anomalous clusters…

In this paper, we define a distance for the HSL colour system. Next, the proposed distance is used for a fuzzy colour clustering algorithm construction. The presented algorithm is related to the well-known fuzzy c-means algorithm. Finally,…