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- W2122532015 abstract "Indexing high dimensional data has its utility in m any real world applications. Especially the informa tion retrieval process is dramatically improved. The existing techniques coul d overcome the problem of “Curse of Dimensionality” of high dimensional data sets by using a technique known as Vector Approxima tion-File which resulted in sub-optimal performance . When compared with VAFile clustering results in more compact data set as it uses inter-dimensional correlations. However, p runing of unwanted clusters is important. The existing pruning techniques are base d on bounding rectangles, bounding hyper spheres ha ve problems in NN search. To overcome this problem Ramaswamy and Rose propose d an approach known as adaptive cluster distance bo unding for high dimensional indexing which also includes an efficie nt spatial filtering. In this paper we implement th is high-dimensional indexing approach. We built a prototype application to for p roof of concept. Experimental results are encouragi ng and the prototype can be used in real time applications." @default.
- W2122532015 created "2016-06-24" @default.
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- W2122532015 date "2013-08-25" @default.
- W2122532015 modified "2023-09-24" @default.
- W2122532015 title "SCALABLE AND EFFICIENT CLUSTER-BASED FRAMEWORK FOR MULTIDIMENSIONAL INDEXING" @default.
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- W2122532015 doi "https://doi.org/10.15623/ijret.2013.0208028" @default.
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