Dimensionally Distributed Density Estimation

被引：5

作者：

Franti, Pasi ^{[1
]}

Sieranoja, Sami ^{[1
]}

机构：

[1] Univ Eastern Finland, Sch Comp, Joensuu, Finland

来源：

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING (ICAISC 2018), PT II | 2018年 / 10842卷

关键词：

Clustering; Density estimation; Density peaks; K-means; K-MEANS ALGORITHM; CLUSTERING-ALGORITHM; INDEX;

D O I：

10.1007/978-3-319-91262-2_31

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Estimating density is needed in several clustering algorithms and other data analysis methods. Straightforward calculation takes O(N-2) because of the calculation of all pairwise distances. This is the main bottleneck for making the algorithms scalable. We propose a faster O(N logN) time algorithm that calculates the density estimates in each dimension separately, and then simply cumulates the individual estimates into the final density values.

引用

页码：343 / 353

页数：11

共 34 条

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[2] Fast density clustering strategies based on the k-means algorithm [J].