hvq  R Documentation 
Hierarchical Vector Quantization
hvq( x, min_compression_perc = NA, n_cells = NA, depth = 3, quant.err = 10, algorithm = "HartiganWong", distance_metric = c("L1_Norm", "L2_Norm"), error_metric = c("mean", "max"), quant_method = c("kmeans", "kmedoids") )
x 
Data Frame. A dataframe of multivariate data. Each row corresponds to an observation, and each column corresponds to a variable. Missing values are not accepted. 
min_compression_perc 
Numeric. An integer indicating the minimum percent compression rate to be achieved for the dataset 
n_cells 
Numeric. Indicating the number of nodes per hierarchy. 
depth 
Numeric. Indicating the hierarchy depth (or) the depth of the tree (1 = no hierarchy, 2 = 2 levels, etc..) 
quant.err 
Numeric. The quantization error for the algorithm. 
algorithm 
String. The type of algorithm used for quantization. Available algorithms are Hartigan and Wong, "Lloyd", "Forgy", "MacQueen". (default is "HartiganWong") 
distance_metric 
character. The distance metric can be 'L1_Norm" or "L2_Norm". L1_Norm is selected by default. 
error_metric 
character. The error metric can be "mean" or "max". mean is selected by default 
quant_method 
character. The quant_method can be "kmeans" or "kmedoids". kmeans is selected by default 
The raw data is first scaled and this scaled data is supplied as input to the vector quantization algorithm. Vector quantization technique uses a parameter called quantization error. This parameter acts as a threshold and determines the number of levels in the hierarchy. It means that, if there are 'n' number of levels in the hierarchy, then all the clusters formed till this level will have quantization error equal or greater than the threshold quantization error. The user can define the number of clusters in the first level of hierarchy and then each cluster in first level is subdivided into the same number of clusters as there are in the first level. This process continues and each group is divided into smaller clusters as long as the threshold quantization error is met. The output of this technique will be hierarchically arranged vector quantized data.
clusters 
List. A list showing each ID assigned to a cluster. 
nodes.clust 
List. A list corresponding to nodes' details. 
idnodes 
List. A list of ID and segments similar to

error.quant 
List. A list of quantization error for all levels and nodes. 
plt.clust 
List. A list of logical values indicating if the quantization error was met. 
summary 
Summary. Output table with summary. 
Shubhra Prakash <shubhra.prakash@musigma.com>, Sangeet Moy Das <sangeet.das@musigma.com>
hvtHmap
data("USArrests",package="datasets") hvqOutput = hvq(USArrests, n_cells = 5, depth = 2, quant.err = 0.2, distance_metric='L1_Norm',error_metric='mean',quant_method="kmeans")
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