Density Conscious Subspace Clustering for High Dimensional Data using Genetic Algorithms
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چکیده
Clustering has been recognized as an important and valuable capability in the data mining field. Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, the identification of dense regions in previous works lacks of considering a critical problem, called “the density divergence problem” in this thesis, which refers to the phenomenon that the region densities vary in different subspace cardinalities. Without considering this problem, previous works utilize a density threshold to discover the dense regions in all subspaces, which incurs the serious loss of clustering accuracy (either recall or precision of the resulting clusters) in different subspace cardinalities. To tackle the density divergence problem, in this thesis, we devise a novel subspace clustering model to discover the
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Density Conscious Subspace Clustering for High Dimensional Data using Genetic Algorithms
Clustering has been recognized as an important and valuable capability in the data mining field. Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, t...
متن کاملDensity Conscious Subspace Clustering for High Dimensional Data using Genetic Algorithms
Clustering has been recognized as an important and valuable capability in the data mining field. Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, t...
متن کاملDensity Conscious Subspace Clustering for High Dimensional Data using Genetic Algorithms
Clustering has been recognized as an important and valuable capability in the data mining field. Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, t...
متن کاملDensity Conscious Subspace Clustering for High Dimensional Data using Genetic Algorithms
Clustering has been recognized as an important and valuable capability in the data mining field. Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, t...
متن کاملDensity Conscious Subspace Clustering for High Dimensional Data using Genetic Algorithms
Clustering has been recognized as an important and valuable capability in the data mining field. Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, t...
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