DATA CLASSIFICATION BASED ON SUPPORTING DATA GRAVITY

被引：3

作者：

Li Junlin ^{[1
]}

Fu Hongguang ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Engn & Comp Sci, Chengdu 610054, Peoples R China

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 1 | 2009年

关键词：

data classification; data gravity; nonlinear separable; angles between vectors;

D O I：

10.1109/ICICISYS.2009.5357940

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper introduces a novel data classification method that is based on the idea of data gravity Many recent clustering and classification ideas based on data gravity tend to consider data gravity magnitude as decisive factor They eye data gravity as scalar quantity Novelly in this paper, data gravity is defined to be a vector, and a vector model is set up to classify data by exploiting the internal structure characteristics among vector points in a class. The proposed method is a nonlinear classification technique that can be applied directly on nonlinear separable data sets without concerning nonlinearity-to-linearity transformation (e g kernel transformation) of the data Experiments have showed the validity and some other useful characteristics of this method

引用

页码：22 / 28

页数：7