CMNugg {PPbigdata}

Central Mass Index for projected Big Data based on data nuggets

Description

This function calculates the value of Central Mass index, a Projection Pursuit index, for projected big data based on data nuggets.

Usage

CMNugg(nuggproj,weight)

Arguments

nuggproj

Projected data nugget centers. Must be a data matrix (of class matrix, or data.frame) or a vector containing only entries of class numeric.

weight

Vector of the weight parameter for each data nugget. Its length should be the same as the number of data nuggets, i.e., nrow(nuggproj) or length(nuggproj). Must be of class numeric or integer.

Details

This function calculates the value of Central Mass index, a Projection Pursuit index for projected Big Data based on data nuggets.

Data nuggets are a representative sample meant to summarize Big Data by reducing a large dataset to a much smaller dataset by eliminating redundant points while also preserving the peripheries of the dataset. Each data nugget is defined by a center (location), weight (importance), and scale (internal variability). Data nuggets for a large dataset could be created and refined by functions create.DN or refine.DN in the package datanugget.

Central Mass index is a kind of Projection Pursuit (PP) index, and larger index values indicate a central mass structure of multivariate data. However, it's computationally hard to calculate the index for big data because of the vector memory limit during calculation. To deal with big data, data nuggets could be used to calculate the index efficiently. In this function, based on the projected data nugget centers with data nugget weights, the Central Mass index is calculated by 1 minus a Hole index based on data nuggets. See HoleNugg

Value

A numeric value indicating Central Mass index value of the projected big data based on the data nuggets.

Author(s)

Yajie Duan, Javier Cabrera

References

Che?rdle, W. K., & Unwin, A. (Eds.). (2007). Handbook of data visualization. Springer Science & Business Media.

Cook, D., Buja, A., Cabrera, J., & Hurley, C. (1995). Grand tour and projection pursuit. Journal of Computational and Graphical Statistics, 4(3), 155-172.

Beavers, T. E., Cheng, G., Duan, Y., Cabrera, J., Lubomirski, M., Amaratunga, D., & Teigler, J. E. (2024). Data Nuggets: A Method for Reducing Big Data While Preserving Data Structure. Journal of Computational and Graphical Statistics, (just-accepted), 1-21.

Duan, Y., Cabrera, J., & Emir, B. (2023). A New Projection Pursuit Index for Big Data. ArXiv:2312.06465. https://doi.org/10.48550/arXiv.2312.06465

See Also

HoleNugg, NHnugg,create.DN, refine.DN

Examples

  require(datanugget)
  require(rstiefel)

  #4-dim small example
  X = cbind.data.frame(rnorm(5*10^3),
                       rnorm(5*10^3,2,1),
                       rnorm(5*10^3,5,2),
                       rnorm(5*10^3))

  #raw data is recommended to be scaled firstly to generate data nuggets for Projection Pursuit
  X = as.data.frame(scale(X))

  #create data nuggets
  my.DN = create.DN(x = X,
                    R = 500,
                    delete.percent = .1,
                    DN.num1 = 500,
                    DN.num2 = 250,
                    no.cores = 0,
                    make.pbs = FALSE)


  #refine data nuggets
  my.DN2 = refine.DN(x = X,
                     DN = my.DN,
                     EV.tol = .9,
                     min.nugget.size = 2,
                     max.splits = 5,
                     no.cores = 0,
                     make.pbs = FALSE)

  #get nugget centers, weights, and scales
  nugg = my.DN2$`Data Nuggets`[,2:(ncol(X)+1)]
  weight = my.DN2$`Data Nuggets`$Weight
  scale = my.DN2$`Data Nuggets`$Scale

  #spherize the data nuggets with weights to calculate the PP index
  nugg_wsph <- wsph(nugg,weight)$data_wsph

  #generate a random orthonormal matrix as a projection matrix to 2-dim space
  proj_2d = rustiefel(4, 2)

  #project data nugget centers into 2-dim space by the random projection matrix
  nuggproj_2d = as.matrix(nugg_wsph)%*%proj_2d

  #plot the projected data nuggets
  #lighter green represents larger weights
  plotNugg(nuggproj_2d, weight)

  #calculate the CM index for the projected 2-dim big data
  CMNugg(nuggproj_2d,weight)