scholarly journals Novel trajectory clustering method based on distance dependent Chinese restaurant process

2019 ◽  
Vol 5 ◽  
pp. e206
Author(s):  
Reza Arfa ◽  
Rubiyah Yusof ◽  
Parvaneh Shabanzadeh
Keyword(s):  
Trajectory Analysis ◽  
Traffic Monitoring ◽  
Transport Systems ◽  
Trajectory Clustering ◽  
Number Of Clusters ◽  
Chinese Restaurant Process ◽  
Chinese Restaurant ◽  
Wide Range ◽  
Path Modelling ◽  
Traditional Approaches

Trajectory clustering and path modelling are two core tasks in intelligent transport systems with a wide range of applications, from modeling drivers’ behavior to traffic monitoring of road intersections. Traditional trajectory analysis considers them as separate tasks, where the system first clusters the trajectories into a known number of clusters and then the path taken in each cluster is modelled. However, such a hierarchy does not allow the knowledge of the path model to be used to improve the performance of trajectory clustering. Based on the distance dependent Chinese restaurant process (DDCRP), a trajectory analysis system that simultaneously performs trajectory clustering and path modelling was proposed. Unlike most traditional approaches where the number of clusters should be known, the proposed method decides the number of clusters automatically. The proposed algorithm was tested on two publicly available trajectory datasets, and the experimental results recorded better performance and considerable improvement in both datasets for the task of trajectory clustering compared to traditional approaches. The study proved that the proposed method is an appropriate candidate to be used for trajectory clustering and path modelling.

scholarly journals Robust Cell Image Segmentation via Improved Markov Random Field Based on a Chinese Restaurant Process Model

2020 ◽  
Vol 24 (7) ◽  
pp. 963-971
Author(s):  
Dongming Li ◽  
Changming Sun ◽  
Su Wei ◽  
Yue Yu ◽  
Jinhua Yang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Random Field ◽  
Markov Random Field ◽  
Process Model ◽  
Mucosal Cell ◽  
Number Of Clusters ◽  
Chinese Restaurant Process ◽  
Cell Image ◽  
Chinese Restaurant ◽  
Markov Random

In this paper, a segmentation method for cell images using Markov random field (MRF) based on a Chinese restaurant process model (CRPM) is proposed. Firstly, we carry out the preprocessing on the cell images, and then we focus on cell image segmentation using MRF based on a CRPM under a maximum a posteriori (MAP) criterion. The CRPM can be used to estimate the number of clusters in advance, adjusting the number of clusters automatically according to the size of the data. Finally, the conditional iteration mode (CIM) method is used to implement the MRF based cell image segmentation process. To validate our proposed method, segmentation experiments are performed on oral mucosal cell images. The segmentation results were compared with other methods, using precision, Dice, and mean square error (MSE) as the objective evaluation criteria. The experimental results show that our method produces accurate cell image segmentation results, and our method can effectively improve segmentation for the nucleus, binuclear cell, and micronucleus cell. This work will play an important role in cell image recognition and analysis.

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 Clustering and the efficient use of cognitive resources

2021 ◽  
Author(s):  
Ishita Dasgupta ◽  
Thomas L. Griffiths
Keyword(s):  
Bayesian Statistics ◽  
Human Intelligence ◽  
Order Structure ◽  
Central Component ◽  
Cognitive Resources ◽  
Chinese Restaurant Process ◽  
Chinese Restaurant ◽  
Wide Range ◽  
Psychological Models ◽  
Clustering Behavior

A central component of human intelligence is the ability to make abstractions, to gloss over some details in favor of drawing out higher-order structure. Clustering stimuli together is a classic example of this. However, the crucial question remains of how one should make these abstractions -- what details to retain and what to throw away? How many clusters to form? We provide an analysis of how a rational agent with limited cognitive resources should approach this problem, considering not only how well a clustering fits the data but also by how 'complex' it is, i.e. how cognitively expensive it is to represent. We show that the solution to this problem provides a way to reinterpret a wide range of psychological models that are based on principles from non-parametric Bayesian statistics. In particular, we show that the Chinese Restaurant Process prior, ubiquitous in models of human and animal clustering behavior, can be interpreted as minimizing an intuitive formulation of representational complexity.

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.

Visualising intrinsic disorder and conformational variation in protein ensembles

2014 ◽  
Vol 169 ◽  
pp. 179-193 ◽  
Author(s):  
Julian Heinrich ◽  
Michael Krone ◽  
Seán I. O'Donoghue ◽  
Daniel Weiskopf
Keyword(s):  
Intrinsic Disorder ◽  
Molecular Graphics ◽  
Post Translational Modifications ◽  
3D Structures ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Wide Range ◽  
Conformational Variations ◽  
Protein Ensembles ◽  
Traditional Approaches

Intrinsically disordered regions (IDRs) in proteins are still not well understood, but are increasingly recognised as important in key biological functions, as well as in diseases. IDRs often confound experimental structure determination—however, they are present in many of the available 3D structures, where they exhibit a wide range of conformations, from ill-defined and highly flexible to well-defined upon binding to partner molecules, or upon post-translational modifications. Analysing such large conformational variations across ensembles of 3D structures can be complex and difficult; our goal in this paper is to improve this situation by augmenting traditional approaches (molecular graphics and principal components) with methods from human–computer interaction and information visualisation, especially parallel coordinates. We present a new tool integrating these approaches, and demonstrate how it can dissect ensembles to reveal functional insights into conformational variation and intrinsic disorder.

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