We introduce a novel interactive framework for visualizing and exploring high-dimensional datasets based on subspace analysis and dynamic projections. We assume the high-dimensional dataset can be represented by a mixture of low-dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from t...

Given a dictionary Π and a signal ξ = Πx generated by a few linearly independent columns of Π, classical sparse recovery theory deals with the problem of uniquely recovering the sparse representation x of ξ. In this work, we consider the more general case where ξ lies in a lowdimensional subspace spanned by a few columns of Π, which are possibly linearly dependent. In this case, x may not uniqu...

In order to establish consolidated standards in novel data mining areas, newly proposed algorithms need to be evaluated thoroughly. Many publications compare a new proposition – if at all – with one or two competitors or even with a so called “näıve” ad hoc solution. For the prolific field of subspace clustering, we propose a software framework implementing many prominent algorithms and, thus, ...

It is often the case that, within an online recommender system, multiple users share a common account. Can such shared accounts be identified solely on the basis of the userprovided ratings? Once a shared account is identified, can the different users sharing it be identified as well? Whenever such user identification is feasible, it opens the way to possible improvements in personalized recomm...

Attempts at clustering large and high dimensional data have been made with a focus on scalability. While still inefficient for more complex problems, the effectiveness is also questionable because data becomes very sparse in a high dimensional space. If clusters exist in the data, they tend to remain hidden in some unidentified sub-spaces. So far, the few solutions to this problem have not been...

In educational research, a fundamental goal is identifying which skills students have mastered, which skills they have not, and which skills they are in the process of mastering. As the number of examinees, items, and skills increases, the estimation of even simple cognitive diagnosis models becomes difficult. We adopt a faster, simpler approach: cluster a capability matrix estimating each stud...

Color is one of the most effective visual variables since it can be combined with other mappings and encode information without using any additional space on the display. An important example where expressing additional visual dimensions is direly needed is the analysis of high-dimensional data. The property of perceptual linearity is desirable in this application, because the user intuitively ...

This paper proposes a subspace clustering algorithm by introducing attribute weights in the affinity propagation algorithm. A new step is introduced to the affinity propagation process to iteratively update the attribute weights based on the current partition of the data. The relative magnitude of the attribute weights can be used to identify the subspaces in which clusters are embedded. Experi...

Subspace clustering mines the clusters present in locally relevant subsets of the attributes. In the literature, several approaches have been suggested along with different measures for quality assessment. Pleiades provides the means for easy comparison and evaluation of different subspace clustering approaches, along with several quality measures specific for subspace clustering as well as ext...

With the increasing availability of data from various domains such as health care, finance, social networks, etc. there is a need to provide analytic tools that are more accessible to lay people. In this paper, we present a software tool which can be used to aid inexperienced users in understanding high dimensional data. To facilitate the understanding of such data, we place special emphasis on...