Background: Chronic kidney disease (CKD), characterized by progressive loss of renal function, is becoming a growing problem in the general population. New analytical technologies such as “omics”-based approaches, including metabolomics, provide a useful platform for biomarker discovery and improvement of CKD management. In metabolomics studies, not only prediction accuracy is ...

To be eeective, data-mining has to conclude with a succinct description of the data. To this end, we explore a clustering technique that nds dense regions in data. By constraining our model in a speciic way, we are able to represent the interesting regions as an intersection of intervals. This has the advantage of being easily read and understood by humans. Speciically, we t the data to a mixtu...

Subspace clustering is a challenging task in the field of data mining. Traditional distance measures fail to differentiate the furthest point from the nearest point in very high dimensional data space. To tackle the problem, we design minimal subspace distance which measures the similarity between two points in the subspace where they are nearest to each other. It can discover subspace clusters...

Clustering is a key data mining problem. Density and grid based technique is a popular way to mine clusters in a large multi-dimensional space wherein clusters are regarded as dense regions than their surroundings. The attribute values and ranges of these attributes characterize the clusters. Fine grid sizes lead to a huge amount of computation while coarse grid sizes result in loss in quality ...

While analyzing some of the complex real-world datasets, it is vital to identify local correlations in the subspaces. Some of the critical limitations of the subspace clustering techniques in identifying order revealing subspace correlation patterns motivate the need for more advanced subspace techniques. We formalize the problem of identifying local nonlinear correlations in high-dimensional d...

The problem of fitting a union of subspaces to a collection of data points drawn from multiple subspaces is considered in this paper. In the traditional low rank representation model, the dictionary used to represent the data points is chosen as the data points themselves and thus the dictionary is corrupted with noise. This problem is solved in the low rank subspace clustering model which deco...

In this paper, we develop a method for unsupervised clustering of twoway (matrix) data by combining two recent innovations from different fields: the Sparse Subspace Clustering (SSC) algorithm [10], which groups points coming from a union of subspaces into their respective subspaces, and the t-product [18], which was introduced to provide a matrix-like multiplication for third order tensors. Ou...

Analyzing uncertain databases is a challenge in data mining research. Usually, data mining methods rely on precise values. In scenarios where uncertain values occur, e.g. due to noisy sensor readings, these algorithms cannot deliver highquality patterns. Beside uncertainty, data mining methods face another problem: high dimensional data. For finding object groupings with locally relevant dimens...