基于快速高斯变换的不确定数据聚类算法

迟荣华; 程媛; 朱素霞; 黄少滨; 陈德运

doi:10.11959/j.issn.1000-436x.2017061

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基于快速高斯变换的不确定数据聚类算法

学术论文 | 更新时间：2024-06-05

- 基于快速高斯变换的不确定数据聚类算法
- Uncertain data analysis algorithm based on fast Gaussian transform
- 通信学报 2017年38卷第3期页码：101-111
- 作者机构：
  
  1. 哈尔滨工程大学计算机科学与技术学院，黑龙江哈尔滨 150001
  2. 哈尔滨理工大学计算机科学与技术学院，黑龙江哈尔滨 150080
  3. 哈尔滨理工大学计算机科学与技术学院博士后流动站，黑龙江哈尔滨 150080
- 作者简介：
  
  [ "迟荣华（1981-），男，黑龙江哈尔滨人，哈尔滨工程大学博士生，主要研究方向为复杂网络、不确定性研究等。" ]
  [ "程媛（1985-），女，黑龙江哈尔滨人，哈尔滨理工大学讲师，主要研究方向为数据挖掘、不确定性研究等。" ]
  [ "朱素霞（1978-），女，山东寿光人，哈尔滨理工大学副教授，主要研究方向为高性能体系结构、并行计算。" ]
  [ "黄少滨（1965-），男，黑龙江哈尔滨人，哈尔滨工程大学教授、博士生导师，主要研究方向为分布式计算与仿真、模型检测、数据集成等。" ]
  [ "陈德运（1962-），男，黑龙江哈尔滨人，哈尔滨理工大学教授、博士生导师，主要研究方向为图像处理、探测和成像技术。" ]
- 基金信息：
  
  国家自然科学青年基金资助项目(61502123);黑龙江省青年科学基金资助项目(QC2015084);黑龙江省普通高等学校青年学术骨干支持计划基金资助项目(1253G017)
- DOI：10.11959/j.issn.1000-436x.2017061
  中图分类号： TP399
- 网络出版日期：2017-03，
  
  纸质出版日期：2017-03-15
- 稿件说明：
移动端阅览
迟荣华, 程媛, 朱素霞, 等. 基于快速高斯变换的不确定数据聚类算法[J]. 通信学报, 2017,38(3):101-111.

Rong-hua CHI, Yuan CHENG, Su-xia ZHU, et al. Uncertain data analysis algorithm based on fast Gaussian transform[J]. Journal on communications, 2017, 38(3): 101-111.
迟荣华, 程媛, 朱素霞, 等. 基于快速高斯变换的不确定数据聚类算法[J]. 通信学报, 2017,38(3):101-111. DOI： 10.11959/j.issn.1000-436x.2017061.

Rong-hua CHI, Yuan CHENG, Su-xia ZHU, et al. Uncertain data analysis algorithm based on fast Gaussian transform[J]. Journal on communications, 2017, 38(3): 101-111. DOI： 10.11959/j.issn.1000-436x.2017061.

摘要

数据中不确定性的存在使对其聚类分析时要充分考虑不确定性的影响。针对现有不确定数据聚类算法中构建不确定数据模型以及距离度量时存在的影响结果准确性与聚类性能等问题，提出一种基于快速高斯变换的不确定数据聚类算法。首先在不假设数据分布的前提下，构建符合不确定性分布特征的数据模型；然后结合不确定对象的2个重要特征：属性特征与表示不确定数据分布特征的概率密度函数，度量不确定数据对象间的相似性；并以此为基础提出不确定数据聚类算法；最后在UCI以及真实数据集上的实验结果表明，所提算法在运行效率和聚类准确性方面均能取得较好效果。

Abstract

The effect of the uncertainties needs to be taken full advantage during uncertain data clustering.An uncertain data clustering algorithm based on fast Gaussian transform was proposed

to solve the problems about the impact on the accuracy of clustering results and the clustering efficiency caused by the uncertainties

during the construction of uncertain data models and the distance measurement

which existed in the current researches.First

the data model according to the characteristic of the uncertainty distribution was constructed

without the premise of assuming the data distribution.And the similarity between uncertain data objects was measured by combining the two important features of uncertain objects

attribute features and the probability density function representing the characteristic of uncertainty distribution.And then the uncertain data clustering algorithm was proposed.Finally

the experiment results on UCI and real datasets indicate the better efficiency and accuracy of proposed algorithm.

关键词

Keywords

references

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