软件导刊2017，Vol.16Issue(2)：7-10,4.DOI:10.11907/rjdk.162466
一种基于地理位置人群分类的非参数聚类方法
The Non-Parametric Clustering Method Based on Group Classification of Geographic Location
邱运芬 1张晖 1李波 1杨春明 2赵旭剑1
作者信息
- 1. 西南科技大学 计算机科学与技术学院,四川 绵阳 621010
- 2. 中国科技技术大学 计算机科学与技术学院,安徽 合肥 230027
- 折叠
摘要
Abstract
Geographical location as the manifestation of user's life,has a pivotal role in the group classification.Due to geographical location data has high-dimensional sparse,the existing classification method must be select feature and determine the characteristics of number in advance,which exist in practical application more inconvenience.To solve this problem,a non-parametric clustering method based on group classification of geographic location was presented.Firstly,use Hierarchical Dirichlet Process unsupervised learning features of the best number;Secondly,use Latent Dirichlet Allocation to feature selection,at the same time get the feature probability matrix;Finally,use it as a clustering weight vector to calculate the similarity between users,using Affinity Propagation implementation group classification.The experimental results show that the method spends less time and less memory,and at the same time with high F-measure.关键词
地理位置/人群分类/分层狄利克雷过程/潜在狄利克雷分布/亲和力聚类Key words
Geographical Location/Group Classification/Hierarchical Dirichlet Process/Latent Dirichlet Allocation/Affinity Propagation分类
信息技术与安全科学引用本文复制引用
邱运芬,张晖,李波,杨春明,赵旭剑..一种基于地理位置人群分类的非参数聚类方法[J].软件导刊,2017,16(2):7-10,4.
