B_Wvalid {BayesCVI}

BCVI-Wiroonsri (WI) index

Description

Compute Bayesian cluster validity index (BCVI) from two to kmax groups using Wiroonsri (WI) as the underling cluster validity index (CVI) with the user's selected Dirichlet prior parameters. The full detail of BCVI can be found in the paper Wiroonsri and Preedasawakul (2024).

Usage

B_Wvalid(x, kmax, method = "kmeans", corr = "pearson", nstart = 100,
      sampling = 1, NCstart = TRUE, alpha = "default", mult.alpha = 1/2)

Arguments

x

a numeric data frame or matrix where each column is a variable to be used for cluster analysis and each row is a data point.

kmax

a maximum number of clusters to be considered.

method

a character string indicating which clustering method to be used ("kmeans", "hclust_complete", "hclust_average", "hclust_single"). The default is "kmeans".

corr

a character string indicating which correlation coefficient is to be computed ("pearson", "kendall" or "spearman"). The default is "pearson".

nstart

a maximum number of initial random sets for kmeans for method = "kmeans". The default is 100.

sampling

a number greater than 0 and less than or equal to 1 indicating the undersampling proportion of data to be used. This argument is intended for handling a large dataset. The default is 1.

NCstart

logical for indexlist includes the "NC", "NCI", "NCI1", and "NCI2"), if TRUE, the NC correlation at k=1 is defined as the ratio introduced in the reference. Otherwise, it is assigned as 0.

alpha

Dirichlet prior parameters \alpha_2,...,\alpha_k where \alpha_k is the parameter corresponding to "the probability of having k groups" (selecting each \alpha_k between 0 to 30 is recommended and using the other parameter mult.alpha to be its multiplier. The default is "default".

mult.alpha

the power s from n^s to be multiplied to the Dirichlet prior parameters alpha (selecting mult.alpha in [0,1) is recommended). The default is \frac{1}{2}.

Details

BCVI-WI is defined as follows. Let

r_k(\bf x) = \dfrac{WI(k)-\min_j WI(j)}{\sum_{i=2}^K (WI(i)-\min_j WI(j))}

Assume that

f({\bf x}|{\bf p}) = C({\bf p}) \prod_{k=2}^Kp_k^{nr_k(x)}

represents the conditional probability density function of the dataset given \bf p, where C({\bf p}) is the normalizing constant. Assume further that {\bf p} follows a Dirichlet prior distribution with parameters {\bm \alpha} = (\alpha_2,\ldots,\alpha_K). The posterior distribution of \bf p still remains a Dirichlet distribution with parameters (\alpha_2+nr_2({\bf x}),\ldots,\alpha_K+nr_K({\bf x})).

The BCVI is then defined as

BCVI(k) = E[p_k|{\bf x}] = \frac{\alpha_k + nr_k({\bf x})}{\alpha_0+n}

where \alpha_0 = \sum_{k=2}^K \alpha_k.

The variance of p_k can be computed as

Var(p_k|{\bf x}) = \dfrac{(\alpha_k + nr_k(x))(\alpha_0 + n -\alpha_k-nr_k(x))}{(\alpha_0 + n)^2(\alpha_0 + n +1 )}.

Value

BCVI

the dataframe where the first and the second columns are the number of groups k and BCVI(k), respectively, for k from 2 to kmax.

VAR

the data frame where the first and the second columns are the number of groups k and the variance of p_k, respectively, for k from 2 to kmax.

CVI

the data frame where the first and the second columns are the number of groups k and the original WI(k), respectively, for k from 2 to kmax.

Author(s)

Nathakhun Wiroonsri and Onthada Preedasawakul

References

N. Wiroonsri, "Clustering performance analysis using a new correlation based cluster validity index," Pattern Recognition, 145, 109910, 2024. doi:10.1016/j.patcog.2023.109910

N. Wiroonsri, O. Preedasawakul, "A Bayesian cluster validity index", arXiv:2402.02162, 2024.

See Also

B2_data, B_TANG.IDX, B_WP.IDX, B_STRPBM.IDX, B_DB.IDX

Examples

library(BayesCVI)

# The data included in this package.
data = B2_data[,1:2]

# alpha
aalpha = c(5,5,5,20,20,20,0.5,0.5,0.5)

B.WI = B_Wvalid(x = scale(data), kmax = 10, method = "kmeans", corr = "pearson",
              nstart = 100, sampling = 1, NCstart = TRUE, alpha = aalpha,
              mult.alpha = 1/2)

# plot the BCVI

pplot = plot_BCVI(B.WI)
pplot$plot_index
pplot$plot_BCVI
pplot$error_bar_plot