A Clustering Density-Based Sample Reduction Method

被引：0

作者：

Mohammadi, Mahdi ^{[1
]}

Raahemi, Bijan ^{[1
]}

Akbari, Ahmad ^{[2
]}

机构：

[1] Univ Ottawa, Ottawa, ON, Canada

[2] Iran Univ Sci & Technol, Dept Comp Engn, Tehran, Iran

来源：

ADVANCES IN ARTIFICIAL INTELLIGENCE, CANADIAN AI 2014 | 2014年 / 8436卷

关键词：

Sample reduction; Clustering; Classification; Density-based; Membership function;

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, we propose a new cluster-based sample reduction method which is unsupervised, geometric, and density-based. The original data is initially divided into clusters, and each cluster is divided into "portions" defined as the areas between two concentric circles. Then, using the proposed geometric-based formulas, the membership value of each sample belonging to a specific portion is calculated. Samples are then selected from the original data according to the corresponding calculated membership value. We conduct various experiments on the NSL-KDD and KDDCup99 datasets.

引用

页码：319 / 325

页数：7

共 10 条

[1]

[Anonymous], 2003, ANAL SURVEY DATA

[2] MEAN SHIFT, MODE SEEKING, AND CLUSTERING [J].