SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Multilevel algorithms for multi-constraint graph partitioning
SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
Efficient k-NN search on vertically decomposed data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Compressing large boolean matrices using reordering techniques
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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We propose a new representation for high-dimensional data that can prove very effective for visualization, nearest neighbor (NN) and range searches. It has been unequivocally demonstrated that existing index structures cannot facilitate efficient search in high-dimensional spaces. We show that a transformation from points to sequences can potentially diminish the negative effects of the dimensionality curse, permitting an efficient NN-search. The transformed sequences are optimally reordered, segmented and stored in a low-dimensional index. The experimental results validate that the proposed representation can be a useful tool for the fast analysis and visualization of high-dimensional databases.