Hamilton et al. have suggested an invaluable scaling formula which describes how the power spectra of density fluctuations evolve into the nonlinear regime of hierarchical clustering. This paper presents an extension of their method to low-density universes and universes with nonzero cosmological constant. We pay particular attention to models with large negative spectral indices, and give a sp...

Current methods for hierarchical clustering of data either operate on features of the data or make limiting model assumptions. We present the hierarchy discovery algorithm (HDA), a model-based hierarchical clustering method based on explicit comparison of joint distributions via Bayesian network learning for predefined groups of data. HDA works on both continuous and discrete data and offers a ...

In view of the extensive evidence of tight inter-relationships between spheroidal galaxies (and galactic bulges) with massive black holes hosted at their centers, a consistent model must deal jointly with the evolution of the two components. We describe one such model, which successfully accounts for the local luminosity function of spheroidal galaxies, for their photometric and chemical proper...

This paper presents a new approach to agglomerative hierarchical clustering. Classical hierarchical clustering algorithms are based on metrics which only consider the absolute distance between two clusters, merging the pair of clusters with highest absolute similarity. We propose a relative dissimilarity measure, which considers not only the distance between a pair of clusters, but also how dis...

In this paper, we propose an approach for the recovery of service abstractions out of sets of available services that play the role of alternative design-decisions, which can be used in a service-oriented application. A service abstraction provides a uniform interface that hides differences in the interfaces of alternative services and consequently allows reducing the coupling between the appli...

A hierarchical multilevel preconditioner is constructed for an efficient solution of a first kind boundary integral equation with the single layer potential operator discretized by a boundary element method. This technique is based on a hierarchical clustering of all boundary elements as used in fast boundary element methods. This hierarchy is applied to define a sequence of nested boundary ele...

We applied hierarchical clustering using Rank distance, previously used in computational stylometry, on literary texts written by Mateiu Caragiale and a number of different authors who attempted to impersonate Caragiale after his death, or simply to mimic his style. Their pastiches were consistently clustered opposite to the original work, thereby confirming the performance of the method and pr...

This paper studies the problem of learning human action attributes based on union-of-subspaces model. It puts forth an extension of the low-rank representation (LRR) model, termed the hierarchical clustering-aware structure-constrained low-rank representation (HCSLRR) model, for unsupervised learning of human action attributes from video data. The effectiveness of the proposed model is demonstr...

This paper discusses the admissibility of agglomerative hierarchical clustering algorithms with respect to space distortion and monotonicity, as defined by Yadohisa et al. and Batagelj, respectively. Several admissibilities and their properties are given for selecting a clustering algorithm. Necessary and sufficient conditions for an updating formula, as introduced by Lance and Williams, are pr...

A widely-used class of algorithms to understand data is hierarchical clustering, but it is often difficult to reconcile the results of these algorithms with hierarchies constructed by humans. Interaction, or querying humans for constraints on the data, is a popular solution for addressing this discrepancy. In this paper, we propose using leave-one-out interactions to achieve better hierarchies ...