Clustering is recognized as sigificant technique for analysing data and concentric effort has been taken in different domains comprises of recognition of pattern, statistical analysis and data mining for decades. Subspace clustering is developed from the group of cluster objects from all subspaces of a dataset. During clustering of objects involing higher dimension, the accuracy and effectivene...

Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerates clusters of objects in all subspaces of a dataset. It tends to produce many over lapping clusters. Approach: Subspace clustering and projected clustering are research areas for clustering in high dimensional spaces. I...

Predictive knowledge discovery is an important knowledge acquisition method. It is also used in the clustering process of data mining. Visualization is very helpful for high dimensional data analysis, but not precise and this limits its usability in quantitative cluster analysis. In this paper, we adopt a visual technique called HOV to explore and verify clustering results with quantified measu...

Large data resources are ubiquitous in science and business. For these domains, an intuitive view on the data is essential to fully exploit the hidden knowledge. Often, these data can be semantically structured by concepts. Since the determination of concepts requires a thorough analysis of the data, data mining methods have to be applied. In the field of subspace clustering, some techniques ha...

We show a variety of ways to cluster student activity datasets using different clustering and subspace clustering algorithms. Our results suggest that each algorithm has its own strength and weakness, and can be used to find clusters of different properties. 1 Background Introduction Many education datasets are by nature high dimensional. Finding coherent and compact clusters becomes difficult ...

We present a novel approach to the subspace clustering problem that leverages ensembles of the K-subspaces (KSS) algorithm via the evidence accumulation clustering framework. Our algorithm forms a co-association matrix whose (i, j)th entry is the number of times points i and j are clustered together by several runs of KSS with random initializations. We analyze the entries of this co-associatio...

A general framework for solving the subspace clustering problem using the CUR decomposition is presented. The CUR decomposition provides a natural way to construct similarity matrices for data that come from a union of unknown subspaces U = M ⋃ i=1 Si. The similarity matrices thus constructed give the exact clustering in the noise-free case. A simple adaptation of the technique also allows clus...

Many clustering algorithms are not applicable to high-dimensional feature spaces, because the clusters often exist only in specific subspaces of the original feature space. Those clusters are also called subspace clusters. In this paper, we propose the algorithm HiSC (Hierarchical Subspace Clustering) that can detect hierarchies of nested subspace clusters, i.e. the relationships of lowerdimens...

This paper considers the problem of subspace clustering under noise. Specifically, we study the behavior of Sparse Subspace Clustering (SSC) when either adversarial or random noise is added to the unlabelled input data points, which are assumed to lie in a union of low-dimensional subspaces. We show that a modified version of SSC is provably effective in correctly identifying the underlying sub...

This paper considers the problem of completing a matrix with many missing entries under the assumption that the columns of the matrix belong to a union of multiple low-rank subspaces. This generalizes the standard low-rank matrix completion problem to situations in which the matrix rank can be quite high or even full rank. Since the columns belong to a union of subspaces, this problem may also ...