Vector Approximation File: Cluster Bounding in High-Dimension Data Set

Provided by: International Journal of Engineering and Advanced Technology (IJEAT)
Topic: Data Management
Format: PDF
In many modern application ranges high-dimensional feature vectors are used to model complex data sets. The authors have proposed an overview about efficient indexing method for high-dimensional database using a 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 their 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.

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