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|Title:||Efficient indexing of high-dimensional data through dimensionality reduction|
|Authors:||Goh, C.H. |
|Citation:||Goh, C.H., Lim, A., Ooi, B.C., Tan, K.-L. (2000). Efficient indexing of high-dimensional data through dimensionality reduction. Data and Knowledge Engineering 32 (2) : 115-130. ScholarBank@NUS Repository. https://doi.org/10.1016/S0169-023X(99)00031-2|
|Abstract:||The performance of the R-tree indexing method is known to deteriorate rapidly when the dimensionality of data increases. In this paper, we present a technique for dimensionality reduction by grouping d distinct attributes into k disjoint clusters and mapping each cluster to a linear space. The resulting k-dimensional space (which may be much smaller than d) can then be indexed using an R-tree efficiently. We present algorithms for decomposing a query region on the native d-dimensional space to corresponding query regions in the k-dimensional space, as well as search and update operations for the `dimensionally-reduced' R-tree. Experiments using real data sets for point, region, and OLAP queries were conducted. The results indicate that there is potential for significant performance gains over a naive strategy in which an R-tree index is created on the native d-dimensional space.|
|Source Title:||Data and Knowledge Engineering|
|Appears in Collections:||Staff Publications|
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