.png){kind=icon}
返回
Generative Models for Evolutionary Clustering
.png){kind=icon}
PRE
AI
DOI：10.1145/2297456.2297459.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article studies evolutionary clustering, a recently emerged hot topic with many important applications, noticeably in dynamic social network analysis. In this article, based on the recent literature on nonparametric Bayesian models, we have developed two generative models: DPChain and HDP-HTM. DPChain is derived from the Dirichlet process mixture (DPM) model, with an exponential decaying component along with the time. HDP-HTM combines the hierarchical dirichlet process (HDP) with a hierarchical transition matrix (HTM) based on the proposed Infinite hierarchical Markov state model (iHMS). Both models substantially advance the literature on evolutionary clustering, in the sense that not only do they both perform better than those in the existing literature, but more importantly, they are capable of automatically learning the cluster numbers and explicitly addressing the corresponding issues. Extensive evaluations have demonstrated the effectiveness and the promise of these two solutions compared to the state-of-the-art literature.
Keyword:
Evolutionary clustering
DPChain
HDP-HTM
iHMS
hierarchical transition matrix
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.8
论文数:
1.3K
被引数:
4.4K
机构
引用论文
分享收藏Sunitinib in the treatment of gastrointestinal stromal tumor: patient selection and perspectives
OAAI
OAAI分享收藏A TUTORIAL ON HIDDEN MARKOV-MODELS AND SELECTED APPLICATIONS IN SPEECH RECOGNITION关于语音识别中的隐马尔可夫模型和选定应用的教程.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏Tinnitus Retraining Therapy (TRT) as a Method for Treatment of Tinnitus and Hyperacusis Patients耳鸣再训练疗法 (TRT) 作为治疗耳鸣和高亢患者的方法
PREAI
分享收藏Smaller, safer, cheaper: One company aims to reinvent the nuclear reactor and save a warming planet
PREAI
分享收藏Preparation and characterization of SiO2/TiO2 core-shell particles with controlled shell thickness
PREAI
分享收藏
查看更多内容