Novel high-dimensional indexing structure based on dual-distance metric
Fei Qing Wu · Journal of Zhejiang University(Engineering Science) · 2007
To speed up high-dimensional similarity search efficiency,a novel high-dimensional indexing structure based on dual distance metric(DDM) was proposed.A four-tuple data structure of the DDM was obtained after modelling.Every point in high-dimensional space was grouped into some clusters using k-means cluster algorithm,then the weightee centroid distance of every point was computed based on the start distance and centroid distance of every point.The index key value of every point was inserted by a partition-based B+-tree,and the index construction algorithm was obtained.Queries in high-dimensional space were transformed into queries in single-dimensional space,and the effects of dimensionality,data size and query request parameter on query performance were investigated.The results show that DDM can effectively reduce search space and the distance computation cost.The index structure is particularly fit for querying largescale high-dimensional data.