Exploiting multi-core and many-core parallelism for subspace clustering - Datta, Amitava; Kaur, Amardeep; Lauer, Tobias; Chabbouh, Sami (Zielona Góra: Uniwersytet Zielonogórski)
Finding clusters in high dimensional data is a challenging research problem. Subspace clustering algorithms aim to find clusters in all possible subspaces of the dataset, where a subspace is a subset of dimensions of the data. But the exponential increase in the number of subspaces with the dimensionality of data renders most of the algorithms inefficient as well as ineffective. Moreover, these algorithms have ingrained data dependency in the clustering process, which means that parallelization becomes difficult and inefficient.
Keywords: data mining; subspace clustering; multi-core; many-core; GPU computing