While subspace clustering emerged as an application of pattern mining and some of its early advances have probably been inspired by developments in pattern mining, over the years both elds progressed rather independently. In this paper, we identify a number of recent developments in pattern mining that are likely to be applicable to alleviate or solve current problems in subspace clustering and...

Most available static data are becoming more and more highdimensional. Therefore, subspace clustering, which aims at finding clusters not only within the full dimension but also within subgroups of dimensions, has gained a significant importance. Recently, OpenSubspace framework was proposed to evaluate and explorate subspace clustering algorithms in WEKA with a rich body of most state of the a...

As a prolific research area in data mining, subspace clustering and related problems induced a vast amount of proposed solutions. However, many publications compare a new proposition – if at all – with one or two competitors or even with a so called “näıve” ad hoc solution but fail to clarify the exact problem definition. As a consequence, even if two solutions are thoroughly compared experimen...

Biomedical experts are increasingly confronted with what is often called Big Data, an important subclass of high-dimensional data. High-dimensional data analysis can be helpful in finding relationships between records and dimensions. However, due to data complexity, experts are decreasingly capable of dealing with increasingly complex data. Mapping higher dimensional data to a smaller number of...

Durch Problemstellungen bei der Anwendung von traditionellen Clustering-Algorithmen auf hochdimensionalen Daten motiviert, wurde im Rahmen meiner Diplomarbeit ein neues algorithmisches Konzept zum effizienten Subspace Clustering entwickelt. Eine mögliche Anwendung dieses Konzeptes stellt die Analyse von CGH Daten dar. Durch Subspace Clustering ist es möglich, Gruppen von Patienten zu identifizi...

In this paper, we present an e↵ective tree based subspace clustering technique (TreeCLUS) for finding clusters in network intrusion data and for detecting known as well as unknown attacks without using any labelled tra c or signatures or training. To establish its e↵ectiveness in finding appropriate number of clusters, we perform a cluster stability analysis. We also introduce an e↵ective clust...

In this material, we provide the theoretical analyses to show that the trivial coefficients always correspond to the codes over errors. Lemmas 1–3 show that our errors-removing strategy will perform well when the lp-norm is enforced over the representation, where p = {1, 2,∞}. Let x 6= 0 be a data point in the union of subspaces SD that is spanned by D = [Dx D−x], where Dx and D−x consist of th...

Subspace clustering separates data points approximately lying on union of affine subspaces into several clusters. This paper presents a novel nonparametric Bayesian subspace clustering model that infers both the number of subspaces and the dimension of each subspace from the observed data. Though the posterior inference is hard, our model leads to a very efficient deterministic algorithm, DP-sp...

Subspace clustering refers to the problem of segmenting a set of data points approximately drawn from a union of multiple linear subspaces. Aiming at the subspace clustering problem, various subspace clustering algorithms have been proposed and low rank representation based subspace clustering is a very promising and efficient subspace clustering algorithm. Low rank representation method seeks ...