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» Forecasting high-dimensional data
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ICDE
1997
IEEE
130views Database» more  ICDE 1997»
15 years 11 months ago
High-Dimensional Similarity Joins
Many emerging data mining applications require a similarity join between points in a high-dimensional domain. We present a new algorithm that utilizes a new index structure, calle...
Kyuseok Shim, Ramakrishnan Srikant, Rakesh Agrawal
62
Voted
DASFAA
2005
IEEE
120views Database» more  DASFAA 2005»
15 years 3 months ago
A New Indexing Method for High Dimensional Dataset
Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering “curse...
Jiyuan An, Yi-Ping Phoebe Chen, Qinying Xu, Xiaofa...
SIGMOD
2000
ACM
165views Database» more  SIGMOD 2000»
15 years 2 months ago
Finding Generalized Projected Clusters In High Dimensional Spaces
High dimensional data has always been a challenge for clustering algorithms because of the inherent sparsity of the points. Recent research results indicate that in high dimension...
Charu C. Aggarwal, Philip S. Yu
INFOVIS
2003
IEEE
15 years 2 months ago
Interactive Hierarchical Dimension Ordering, Spacing and Filtering for Exploration of High Dimensional Datasets
Large numbers of dimensions not only cause clutter in multidimensional visualizations, but also make it difficult for users to navigate the data space. Effective dimension manage...
Jing Yang, Wei Peng, Matthew O. Ward, Elke A. Rund...
HAIS
2009
Springer
15 years 2 months ago
Unsupervised Feature Selection in High Dimensional Spaces and Uncertainty
Developing models and methods to manage data vagueness is a current effervescent research field. Some work has been done with supervised problems but unsupervised problems and unce...
José Ramón Villar, María del ...