scholarly journals The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies

2010 ◽  
Vol 57 (2) ◽  
pp. 1-30 ◽  
Author(s):  
David M. Blei ◽  
Thomas L. Griffiths ◽  
Michael I. Jordan
Keyword(s):  
Nonparametric Inference ◽  
Bayesian Nonparametric ◽  
Chinese Restaurant Process ◽  
Chinese Restaurant

scholarly journals CRPClustering: An R Package for Bayesian Nonparametric Chinese Restaurant Process Clustering with Entropy

2018 ◽  
Author(s):  
Masashi Okada
Keyword(s):  
Dirichlet Process ◽  
Scientific Method ◽  
R Package ◽  
Bayesian Nonparametrics ◽  
Bayesian Nonparametric ◽  
Number Of Clusters ◽  
Chinese Restaurant Process ◽  
Chinese Restaurant

Clustering is a scientific method which finds the clusters of data and many related methods are traditionally researched for long terms. Bayesian nonparametrics is statistics which can treat models having infinite parameters. Chinese restaurant process is used in order to compose Dirichlet process. The clustering which uses Chinese restaurant process does not need to decide the number of clusters in advance. This algorithm automatically adjusts it. Then, this package can calculate clusters in addition to entropy as the ambiguity of clusters.

scholarly journals CRPClustering: An R Package for Bayesian Nonparametric Chinese Restaurant Process Clustering with Entropy

2018 ◽  
Author(s):  
Masashi Okada
Keyword(s):  
Dirichlet Process ◽  
Scientific Method ◽  
R Package ◽  
Bayesian Nonparametrics ◽  
Bayesian Nonparametric ◽  
Number Of Clusters ◽  
Chinese Restaurant Process ◽  
Chinese Restaurant

Clustering is a scientific method which finds the clusters of data and many related methods are traditionally researched for long terms. Bayesian nonparametrics is statistics which can treat models having infinite parameters. Chinese restaurant process is used in order to compose Dirichlet process. The clustering which uses Chinese restaurant process does not need to decide the number of clusters in advance. This algorithm automatically adjusts it. Then, this package can calculate clusters in addition to entropy as the ambiguity of clusters.

scholarly journals CRPClustering: An R Package for Bayesian Nonparametric Chinese Restaurant Process Clustering with Entropy

2018 ◽  
Author(s):  
Masashi Okada
Keyword(s):  
Dirichlet Process ◽  
Scientific Method ◽  
R Package ◽  
Bayesian Nonparametrics ◽  
Bayesian Nonparametric ◽  
Number Of Clusters ◽  
Chinese Restaurant Process ◽  
Chinese Restaurant

Clustering is a scientific method which finds the clusters of data and many related methods are traditionally researched for long terms. Bayesian nonparametrics is statistics which can treat models having infinite parameters. Chinese restaurant process is used in order to compose Dirichlet process. The clustering which uses Chinese restaurant process does not need to decide the number of clusters in advance. This algorithm automatically adjusts it. Then, this package can calculate clusters in addition to entropy as the ambiguity of clusters.

Bayesian nonparametric inference for the overlap of daily animal activity patterns

2018 ◽  
Vol 25 (4) ◽  
pp. 471-494 ◽  
Author(s):  
Gabriel Núñez-Antonio ◽  
Manuel Mendoza ◽  
Alberto Contreras-Cristán ◽  
Eduardo Gutiérrez-Peña ◽  
Eduardo Mendoza
Keyword(s):  
Activity Patterns ◽  
Nonparametric Inference ◽  
Bayesian Nonparametric ◽  
Animal Activity

Bayesian nonparametric inference beyond the Gibbs-type framework

2018 ◽  
Vol 45 (4) ◽  
pp. 1062-1091 ◽  
Author(s):  
Federico Camerlenghi ◽  
Antonio Lijoi ◽  
Igor Prünster
Keyword(s):  
Nonparametric Inference ◽  
Bayesian Nonparametric

scholarly journals A Distance-Dependent Chinese Restaurant Process Based Method for Event Detection on Social Media

Inventions ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 80 ◽  
Author(s):  
Georgios Palaiokrassas ◽  
Athanasios Voulodimos ◽  
Antonios Litke ◽  
Athanasios Papaoikonomou ◽  
Theodora Varvarigou
Keyword(s):  
Social Media ◽  
Event Detection ◽  
Dirichlet Process ◽  
Chinese Restaurant Process ◽  
Chinese Restaurant ◽  
Social Events ◽  
The Social ◽  
Social Event Detection ◽  
Clustering Approach ◽  
Processing Steps

In this paper, we propose a method for event detection on social media, which aims at clustering media items into groups of events based on their textural information as well as available metadata. Our approach is based on distance-dependent Chinese Restaurant Process (ddCRP), a clustering approach resembling Dirichlet process algorithm. Furthermore, we scrutinize the effectiveness of a series of pre-processing steps in improving the detection performance. We experimentally evaluated our method using the Social Event Detection (SED) dataset of MediaEval 2013 benchmarking workshop, which pertains to the discovery of social events and their grouping in event-specific clusters. The obtained results indicate that the proposed method attains very good performance rates compared to existing approaches.

Sign in / Sign up

Export Citation Format

Share Document