An Indexing Structure for Dynamic Multidimensional Data in Vector Space

被引：0

作者：

Mikhaylova, Elena ^{[1
]}

Novikov, Boris ^{[1
]}

Volokhov, Anton ^{[1
]}

机构：

[1] St Petersburg State Univ, St Petersburg, Russia

来源：

ADVANCES IN DATABASES AND INFORMATION SYSTEMS | 2013年 / 186卷

关键词：

HIGH-DIMENSIONAL DATA; NEAREST-NEIGHBOR; M-TREE; ALGORITHM; QUERIES;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The multidimensional k - NN (k nearest neighbors) query problem is relevant to a large variety of database applications, including information retrieval, natural language processing, and data mining To solve it efficiently, the database needs an indexing structure that provides this kind of search. However, attempts to find an exact solution are hardly feasible in multidimensional space. In this paper, a novel indexing technique for the approximate solution of k - NN problem is described and analyzed. The construction of the indexing tree is based on clustering. Indexing structure is implemented on top of high-performance industrial DBMS.

引用

页码：185 / 193

页数：9

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