: In this article, we propose an efficient and effective method for finding arbitrarily oriented subspace clusters by mapping the data space to a parameter space defining the set o...
— In recent applications of clustering such as gene expression microarray analysis, collaborative filtering, and web mining, object similarity is no longer measured by physical ...
Finding latent patterns in high dimensional data is an important research problem with numerous applications. The most well known approaches for high dimensional data analysis are...
Abstract. Finding correlation clusters in the arbitrary subspaces of highdimensional data is an important and a challenging research problem. The current state-of-the-art correlati...
Subspace clustering and frequent itemset mining via “stepby-step” algorithms that search the subspace/pattern lattice in a top-down or bottom-up fashion do not scale to large ...