We present the results of a thorough evaluation of the subspace clustering algorithm SEPC using the OpenSubspace framework. We show that SEPC outperforms competing projected and subspace clustering algorithms on synthetic and some real world data sets. We also show that SEPC can be used to effectively discover clusters with overlapping objects (i.e., subspace clustering).

Swarm Based clustering (SBC) is a promising nature-inspired technique. A swarm of stochastic agents performs the task of clustering high-dimensional data on a low-dimensional output space. Most SBC methods are derivatives of the Ant Colony Clustering (ACC) approach proposed by Lumer and Faieta. Compared to clustering on Emergent Self-Organizing Maps (ESOM) these methods usually perform poorly i...

High dimensional data is often analysed resorting to its distribution properties in subspaces. Subspace clustering is a powerfull method for elicication of high dimensional data features. The result of subspace clustering can be an essential base for building indexing structures and further data search. However, a high number of subspaces and data instances can conceal a high number of subspace...

Many real applications are required to detect outliers in high dimensional data sets. The major difficulty of mining outliers lies on the fact that outliers are often embedded in subspaces. No efficient methods are available in general for subspace-based outlier detection. Most existing subspacebased outlier detection methods identify outliers by searching for abnormal sparse density units in s...

An efficient Actionable 3D Subspace Clustering based on Optimal Centroids from continuous valued data represented three dimensionally which is suitable for real world problems profitable stocks discovery , biologically significant protein residues etc. It achieves actionable patterns ,incorporation of domain knowledge which allows users to choose the preferred utility(profit/benefit) function, ...

Clustering is an established data mining technique for grouping objects based on their mutual similarity. Since in today’s applications, however, usually many characteristics for each object are recorded, one cannot expect to find similar objects by considering all attributes together. In contrast, valuable clusters are hidden in subspace projections of the data. As a general solution to this p...

We present a novel and highly effective approach for multi-body motion segmentation. Drawing inspiration from robust statistical model fitting, we estimate putative subspace hypotheses from the data. However, instead of ranking them we encapsulate the hypotheses in a novel Mercer kernel which elicits the potential of two point trajectories to have emerged from the same subspace. The kernel perm...

Statistical inferences for sample correlation matrices are important in high dimensional data analysis. Motivated by this, this paper establishes a new central limit theorem (CLT) for a linear spectral statistic (LSS) of high dimensional sample correlation matrices for the case where the dimension p and the sample size n are comparable. This result is of independent interest in large dimensiona...

In high-dimensional data analysis, penalized likelihood estimators are shown to provide superior results in both variable selection and parameter estimation. A new algorithm, APPLE, is proposed for calculating the Approximate Path for Penalized Likelihood Estimators. Both convex penalties (such as LASSO) and folded concave penalties (such as MCP) are considered. APPLE efficiently computes the s...

In this work, we address the problem of contextual recommendations by exploiting the concept of fault-tolerant subspace clustering. Specifically, we pre-partition users that have similarly rated subsets of data items into clusters and associate with each cluster a context situation. Context is defined as any internally stored information that can be used to characterize the data per se. Then, g...