scholarly journals Characterization of a Bayesian genetic clustering algorithm based on a Dirichlet process prior and comparison among Bayesian clustering methods

2011 ◽  
Vol 12 (1) ◽  
Author(s):  
Akio Onogi ◽  
Masanobu Nurimoto ◽  
Mitsuo Morita
Keyword(s):  
Dirichlet Process ◽  
Clustering Algorithm ◽  
Bayesian Clustering ◽  
Clustering Methods ◽  
Dirichlet Process Prior ◽  
Genetic Clustering

CLUSTERING USING AN IMPROVED HYBRID GENETIC ALGORITHM

2007 ◽  
Vol 16 (06) ◽  
pp. 919-934
Author(s):  
YONGGUO LIU ◽  
XIAORONG PU ◽  
YIDONG SHEN ◽  
ZHANG YI ◽  
XIAOFENG LIAO
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Sum Of Squares ◽  
Clustering Methods ◽  
Clustering Problem ◽  
Mutation Operation ◽  
Iteration Methods ◽  
Genetic Clustering ◽  
The Individual

In this article, a new genetic clustering algorithm called the Improved Hybrid Genetic Clustering Algorithm (IHGCA) is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGCA, the improvement operation including five local iteration methods is developed to tune the individual and accelerate the convergence speed of the clustering algorithm, and the partition-absorption mutation operation is designed to reassign objects among different clusters. By experimental simulations, its superiority over some known genetic clustering methods is demonstrated.

A matrix-variate dirichlet process to model earthquake hypocentre temporal patterns

2020 ◽  
pp. 1471082X2093976
Author(s):  
Meredith A. Ray ◽  
Dale Bowman ◽  
Ryan Csontos ◽  
Roy B. Van Arsdale ◽  
Hongmei Zhang
Keyword(s):  
Dirichlet Process ◽  
Temporal Pattern ◽  
Temporal Trends ◽  
Temporal Patterns ◽  
The United States ◽  
Seismic Zone ◽  
Clustering Methods ◽  
Dirichlet Process Prior ◽  
And Performance ◽  
Over Time

Earthquakes are one of the deadliest natural disasters. Our study focuses on detecting temporal patterns of earthquakes occurring along intraplate faults in the New Madrid seismic zone (NMSZ) within the middle of the United States from 1996–2016. Based on the magnitude and location of each earthquake, we developed a Bayesian clustering method to group hypocentres such that each group shared the same temporal pattern of occurrence. We constructed a matrix-variate Dirichlet process prior to describe temporal trends in the space and to detect regions showing similar temporal patterns. Simulations were conducted to assess accuracy and performance of the proposed method and to compare to other commonly used clustering methods such as Kmean, Kmedian and partition-around-medoids. We applied the method to NMSZ data to identify clusters of temporal patterns, which represent areas of stress that are potentially migrating over time. This information can then be used to assist in the prediction of future earthquakes.

Ant Custering Algorithms

2010 ◽  
Vol 1 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Yu-Chiun Chiou ◽  
Shih-Ta Chou
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Small Scale ◽  
Solution Stability ◽  
Clustering Methods ◽  
Scale Problem ◽  
Clustering Problem ◽  
Genetic Clustering ◽  
Fully Connected ◽  
Pheromone Trail

This paper proposes three ant clustering algorithms (ACAs): ACA-1, ACA-2 and ACA-3. The core logic of the proposed ACAs is to modify the ant colony metaheuristic by reformulating the clustering problem into a network problem. For a clustering problem of N objects and K clusters, a fully connected network of N nodes is formed with link costs, representing the dissimilarity of any two nodes it connects. K ants are then to collect their own nodes according to the link costs and following the pheromone trail laid by previous ants. The proposed three ACAs have been validated on a small-scale problem solved by a total enumeration method. The solution effectiveness at different problem scales consistently shows that ACA-2 outperforms among these three ACAs. A further comparison of ACA-2 with other commonly used clustering methods, including agglomerative hierarchy clustering algorithm (AHCA), K-means algorithm (KMA) and genetic clustering algorithm (GCA), shows that ACA-2 significantly outperforms them in solution effectiveness for the most of cases and also performs considerably better in solution stability as the problem scales or the number of clusters gets larger.

scholarly journals NML, Bayes and true distributions

2019 ◽  
Author(s):  
Peter Grünwald ◽  
Danielle Navarro
Keyword(s):  
Dirichlet Process ◽  
Minimum Description Length ◽  
Mathematical Form ◽  
Bayesian Decision ◽  
Dirichlet Process Prior ◽  
Information Theoretic ◽  
Normalized Maximum Likelihood ◽  
Competing Models ◽  
Theoretic Characterization

We review the normalized maximum likelihood (NML) criterion for selecting among competing models. NML is generally justified on information-theoretic grounds, via the principle of minimum description length (MDL), in a derivation that “does not assume the existence of a true, data-generating distribution.” Since this “agnostic” claim has been a source of some recent confusion in the psychological literature, we explain in detail what is meant by this statement. In doing so we discuss the work presented by Karabatsos and Walker (2006), who propose an alternative Bayesian decision-theoretic characterization of NML, which leads them to conclude that the claim of agnosticity is meaningless. In the KW derivation, one part of the NML criterion (the likelihood term) arises from placing a Dirichlet process prior over possible data-generating distributions, and the other part (the complexity term) is folded into a loss function. Whereas in the original derivations of NML, the complexity term arises naturally, in the KW derivation its mathematical form is taken for granted and not explained any further. We argue that for this reason, the KW characterization is incomplete; relatedly, we question the relevance of the characterization and we argue that their main conclusion about agnosticity does not follow.

Ant Custering Algorithms

2012 ◽  
pp. 1-15
Author(s):  
Yu-Chiun Chiou ◽  
Shih-Ta Chou
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Small Scale ◽  
Solution Stability ◽  
Clustering Methods ◽  
Clustering Problem ◽  
The Core ◽  
Genetic Clustering ◽  
Fully Connected ◽  
Pheromone Trail

This paper proposes three ant clustering algorithms (ACAs): ACA-1, ACA-2 and ACA-3. The core logic of the proposed ACAs is to modify the ant colony metaheuristic by reformulating the clustering problem into a network problem. For a clustering problem of N objects and K clusters, a fully connected network of N nodes is formed with link costs, representing the dissimilarity of any two nodes it connects. K ants are then to collect their own nodes according to the link costs and following the pheromone trail laid by previous ants. The proposed three ACAs have been validated on a small-scale problem solved by a total enumeration method. The solution effectiveness at different problem scales consistently shows that ACA-2 outperforms among these three ACAs. A further comparison of ACA-2 with other commonly used clustering methods, including agglomerative hierarchy clustering algorithm (AHCA), K-means algorithm (KMA) and genetic clustering algorithm (GCA), shows that ACA-2 significantly outperforms them in solution effectiveness for the most of cases and also performs considerably better in solution stability as the problem scales or the number of clusters gets larger.

Empirical Evaluation of Genetic Clustering Methods Using Multilocus Genotypes From 20 Chicken Breeds

Genetics ◽  
2001 ◽  
Vol 159 (2) ◽  
pp. 699-713
Author(s):  
Noah A Rosenberg ◽  
Terry Burke ◽  
Kari Elo ◽  
Marcus W Feldman ◽  
Paul J Freidlin ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Population Structure ◽  
Clustering Algorithm ◽  
Empirical Evaluation ◽  
Unknown Origin ◽  
Clustering Methods ◽  
Genetic Cluster ◽  
Data Set ◽  
Multilocus Genotypes ◽  
Chicken Breeds

Abstract We tested the utility of genetic cluster analysis in ascertaining population structure of a large data set for which population structure was previously known. Each of 600 individuals representing 20 distinct chicken breeds was genotyped for 27 microsatellite loci, and individual multilocus genotypes were used to infer genetic clusters. Individuals from each breed were inferred to belong mostly to the same cluster. The clustering success rate, measuring the fraction of individuals that were properly inferred to belong to their correct breeds, was consistently ~98%. When markers of highest expected heterozygosity were used, genotypes that included at least 8–10 highly variable markers from among the 27 markers genotyped also achieved >95% clustering success. When 12–15 highly variable markers and only 15–20 of the 30 individuals per breed were used, clustering success was at least 90%. We suggest that in species for which population structure is of interest, databases of multilocus genotypes at highly variable markers should be compiled. These genotypes could then be used as training samples for genetic cluster analysis and to facilitate assignments of individuals of unknown origin to populations. The clustering algorithm has potential applications in defining the within-species genetic units that are useful in problems of conservation.

scholarly journals Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis

2018 ◽  
Vol 11 (3) ◽  
pp. 52 ◽  
Author(s):  
Mark Jensen ◽  
John Maheu
Keyword(s):  
Dirichlet Process ◽  
Joint Distribution ◽  
Nonparametric Model ◽  
Bayesian Nonparametric ◽  
Excess Returns ◽  
Dirichlet Process Prior ◽  
Stock Market Returns ◽  
Conditional Mean ◽  
Risk Return ◽  
Volatility Feedback

In this paper, we let the data speak for itself about the existence of volatility feedback and the often debated risk–return relationship. We do this by modeling the contemporaneous relationship between market excess returns and log-realized variances with a nonparametric, infinitely-ordered, mixture representation of the observables’ joint distribution. Our nonparametric estimator allows for deviation from conditional Gaussianity through non-zero, higher ordered, moments, like asymmetric, fat-tailed behavior, along with smooth, nonlinear, risk–return relationships. We use the parsimonious and relatively uninformative Bayesian Dirichlet process prior to overcoming the problem of having too many unknowns and not enough observations. Applying our Bayesian nonparametric model to more than a century’s worth of monthly US stock market returns and realized variances, we find strong, robust evidence of volatility feedback. Once volatility feedback is accounted for, we find an unambiguous positive, nonlinear, relationship between expected excess returns and expected log-realized variance. In addition to the conditional mean, volatility feedback impacts the entire joint distribution.

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