Fractal dimension and similarity search in high-dimensional spatial databases

被引：0

作者：

Malcok, Mehmet ^{[1
]}

Aslandogan, Y. Alp. ^{[2
]}

Yesildirek, Aydin ^{[3
]}

机构：

[1] Behrend Coll, Dept Comp Sci, Erie, PA 16563 USA

[2] Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76019 USA

[3] Gannan Univ, Dept Elect Engn, Erie, PA 16563 USA

来源：

IRI 2006: PROCEEDINGS OF THE 2006 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION | 2006年

关键词：

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：

In this paper the relationship between the dimension of the address space and the intrinsic ("fractal") dimension of the data set is investigated An estimate of a lower bound for the number of features needed in a similarity search is given and it is shown that this bound is a function of the intrinsic dimension of the data set. Our result indicates the "deflation" of the dimensionality curse in fractal data sets by showing the explicit relationship between the intrinsic dimension of the data set and the embedding dimension of the address space. More precisely, we show that the relationship between the intrinsic dimension and the embedded dimension is linear

引用

页码：380 / +

页数：2

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