Vector Approximation File: Cluster Bounding in High-Dimension Data Set
Poonam Yerpude, Computer Science Department and Engg. RCET Bhiali, CSVTU Raipur, India.
Manuscript received on November 02, 2011. | Revised Manuscript received on December 16, 2011. | Manuscript published on December 30, 2011. | PP: 126-130 | Volume-1 Issue-2, December 2011. | Retrieval Number: B0161121211/2011©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In many modern application ranges high-dimensional feature vectors are used to model complex data sets. We have proposed an overview about efficient indexing method for high-dimensional database using an filtering approach known as vector approximation approach which supports the nearest neighbor search efficiently And A cluster distance bound based on separating hyper planes, that complements our index in electively retrieving clusters that contain data entries closest to the query. The Creation of approximation for Vectors for use in similarity (also known the retrieval of k-nearest neighbor) is examined.
Keywords: Similarity Search, indexing, vector quantization, clustering, Nearest Neighbor search.