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 ...

I review the main steps made so far towards a detailed (semi) analytical model for the hierarchical clustering of bound virialized objects (i.e., haloes) in the gravitational instability scenario. I focus on those models relying on the spherical collapse approximation which have led to the most complete description. The work is divided in two parts: a first one dealing with the mass function of...

this paper presents results of hydro-chemical processes controlling groundwater chemical composition, using an integrated application of hierarchical cluster analysis and factor analysis of a major ion data set of groundwater from mighan playa aquifer. cluster analysis classified samples into four clusters(a, b, c and d) according to their dominant chemical composition: cluster a (dominant comp...

We present an approach for the automatic induction of concept hierarchies from text collections. We propose a novel guided agglomerative hierarchical clustering algorithm exploiting a hypernym oracle to drive the clustering process. By inherently integrating the hypernym oracle into the clustering algorithm, we overcome two main problems of unsupervised clustering approaches relying on the dist...

An attempt is made to create some statistical tests for comparing results of hierarchical cluster analysis based on the uniform distribution over the set of all possible dendrograms. Three different uniform distributions are considered according to the degree of similarity of the dendrograms. Some distances between dendrograms are defined and The solutions proposed are computational and are bas...

For hierarchical clustering, dendrograms provide convenient and powerful visualization. Although many visualization methods have been suggested for partitional clustering, their usefulness deteriorates quickly with increasing dimensionality of the data and/or they fail to represent structure between and within clusters simultaneously. In this paper we extend (dissimilarity) matrix shading with ...

For hierarchical clustering, dendrograms provide convenient and powerful visualization. Although many visualization methods have been suggested for partitional clustering, their usefulness deteriorates quickly with increasing dimensionality of the data and/or they fail to represent structure between and within clusters simultaneously. In this paper we extend (dissimilarity) matrix shading with ...

Organizing data into hierarchies is natural for humans. However, there is little work in machine learning that explores human-machine mixed-initiative approaches to organizing data into hierarchical clusters. In this paper we consider mixed-initiative clustering of a user's email, in which the machine produces (initial and re-trained) hierarchical clusterings of email, and the user iteratively ...