sClust (version 1.0)

recursClust: Perform a multi level clustering

Description

The function, for a given dataFrame, will separate the data using the input clustering method in several levels.

Usage

recursClust(
  dataFrame,
  levelMax = 2,
  clustFunction,
  similarity = TRUE,
  vois = 7,
  flagDiagZero = FALSE,
  biparted = FALSE,
  method = "default",
  tolerence = 0.99,
  threshold = 0.9,
  minPoint = 7,
  verbose = FALSE,
  ...
)

Arguments

dataFrame

The dataFrame.

levelMax

The maximum depth level.

clustFunction

the clustering function to apply on data.

similarity

if True, will use the similarity matrix for the clustering function.

vois

number of points that will be selected for the similarity computation.

flagDiagZero

if True, Put zero on the similarity matrix W.

biparted

if True, the function will not automatically choose the number of clusters to compute.

method

The method that will be used. "default" to let the function choose the most suitable method. "PEV" for the Principal EigenValue method. "GAP" for the GAP method.

tolerence

The tolerance allowed for the Principal EigenValue method.

threshold

The threshold to select the dominant eigenvalue for the GAP method.

minPoint

The minimum number of points required to compute a cluster.

verbose

To output the verbose in the terminal.

...

additional arguments for the clustering function.

Value

returns a list containing the following elements:

• cluster: vector that contain the result of the last level

• allLevels: dataframe containing the clustering results of each levels

• nbLevels: the number of computed levels

Examples

# NOT RUN {
### Example 1: 2 disks of the same size
n<-100 ; r1<-1
x<-(runif(n)-0.5)*2;
y<-(runif(n)-0.5)*2
keep1<-which((x*2+y*2)<(r1*2))
disk1<-data.frame(x+3*r1,y)[keep1,]
disk2 <-data.frame(x-3*r1,y)[keep1,]
sameTwoDisks <- rbind(disk1,disk2)
res <- recursClust(scale(sameTwoDisks),levelMax=3, clustFunction =ShiMalikSC,
                   similarity = TRUE, vois = 7, flagDiagZero = FALSE,
                   biparted = TRUE, verbose = TRUE)
plot(sameTwoDisks, col = as.factor(res$cluster))

### Example 2: Speed and Stopping Distances of Cars
res <- recursClust(scale(iris[,-5]),levelMax=4, clustFunction = spectralPAM,
                   similarity = TRUE, vois = 7, flagDiagZero = FALSE,
                   biparted = FALSE, method = "PEV", tolerence =  0.99,
                   threshold = 0.9, verbose = TRUE)
plot(iris, col = as.factor(res$cluster))
# }