Display Name

Variable Name

I/O and Type

Default Value

Description

Variables

irng

Input
Range

<active>

Select data range for the hierarchical cluster analysis. Note that beginning with Origin 2020b, there is a shortened syntax that follows the form [Book]Sheet!(N1:N2), N1 = the beginning column index and N2 being the ending column index in a contiguous range of columns. More complex strings from noncontiguous data of the form [Book]Sheet!([Book]Sheet!N1:N2,[Book]Sheet!N3:N4) are also possible.

Observation Labels

label

Input
Range

<optional>

Select labels for observations. If labels are chosen, they will be shown as ticks of X axis in the dendrogram. This option is enabled only when obj is Observations.

Cluster

obj

Input
int

0

Specify the type of objects to cluster.
Option list:
 Observations
 Cluster observations.
 Variables
 Cluster variables.

Cluster Method

link

Input
int

2

Select the linkage method to calculate the distance between a cluster and a new cluster. Values start from 0, but string values (such as near) are recommended for clarity.
Option list:
 near:Nearest neighbor
 The minimum of two distances between a cluster and two clusters merged to a new cluster. Also called single linkage.
 furth:Furthest neighbor
 The maximum of distances between a cluster and two clusters merged to a new cluster. Also called complete linkage.
 group:Group average
 The mean of two distances between a cluster and two clusters merged to a new cluster.
 centroid:Centroid
 Clusters are produced that maximize the distance between the centers of clusters.
 median:Median
 The median distance between an item in one cluster and an item in the other cluster.
 ward:Ward
 Clusters are produced that minimize the withincluster variance.
To learn more about linkage methods, see the algorithm of linkage methods.

Distance Type

dist1

Input
int

0

Select a distance type in the hierarchical cluster analysis when obj is Observations. Values start from 0, but string values (such as euc) are recommended for clarity.
Option list:
 euc:Euclidean
 The square root of the sum of the squared differences between two observations.
 squ:Squared Euclidean
 The sum of the squared differences between two observations.
 city:City block
 The sum of the absolute differences between two observations. Also known as Manhattan distance.

Distance Type

dist2

Input
int

0

Select a distance type in the hierarchical cluster analysis when obj is Variables. Values start from 0, but string values (such as corr) are recommended for clarity.
Option list:
 corr:Correlation
 The difference between 1 and the correlation of two variables.
 abs:Absolute correlation
 The difference between 1 and the absolute correlation of two variables.

Standardize Variables

std

Input
int

0

Specify the method to standardize variables. It is available only when obj is Observations. Values start from 0, but string values (such as snd) are recommended for clarity.
Option list:
 none:None
 Variables are not standardized.
 snd:Z scores (standardize to N(0, 1))
 Variables are transformed to the standard normal distribution.
 range:Normalize to (0, 1)
 Variable are transformed to the range of 0 and 1

Number of Clusters

number

Input
int

1

Specify the number of clusters.

Find Clustroid by

stat

Input
int

0

Specify the method to find the clustroid: the most/least representative variable/observation.
Option list:
 sd:Sum of distances
 Find Clustroid using the sum of distances measured from all other observations/variables in the cluster.
 md:Maximum distance
 Find Clustroid using the Maximum distance among all distances measured from other observations/variables in the cluster.
 ssd:Sum of squares of distances
 Find Clustroid using the sum of the squares of distances measured from all other observations/variables in the cluster.

Dissimilarity Matrix

dissimilarity

Input
int

0

Specify whether to output the distance matrix. For a large number of objects, the distance matrix will be shown in a sheet instead of the report. 1 = Yes, 0 = No.

Cluster Stages

stage

Input
int

1

Specify whether to output the cluster stages. 1 = Yes, 0 = No.

Cluster Center

center

Input
int

0

Specify whether to calculate cluster centers. It is available only when obj is Observations. 1 = Yes, 0 = No.

Distance between Cluster Centers

distc2c

Input
int

0

Specify whether to calculate the distances between cluster centers. It is available only when obj is Observations. 1 = Yes, 0 = No.

Distance between Observations and Clusters

disto2c

Input
int

0

Specify whether to calculate the distance between each observation and cluster centers. It is available only when obj is Observations. 1 = Yes, 0 = No.

Dendrogram

dendrogram

Input
int

1

Specify whether to show the dendrogram. 1 = Yes, 0 = No.

Show Dendrogram

ngraph

Input
int

0

Specify whether to show the dendrogram in a single graph or in separate graphs for clusters. It is enabled only when dendrogram is 1. Values start from 0.
Option list:
 Show the dendrogram in a single graph. Different clusters are shown in different colors.
 Show the dendrogram in separate graphs for clusters. Each graph represents a cluster.

Orientation

orient

Input
int

0

Specify the orientation of the dendrogram. Enabled only when dendrogram is Yes.
Option List:
 0: Vertical
 Plot Dendrogram vertically.
 1: Horizontal
 Plot Dendrogram horizontally.
 2: Circular
 Plot circular Dendrogram

Cluster Report

rt

Output
ReportTree

<new>

Specify the sheet for the hierarchical cluster analysis report.

Cluster Membership

rd

Output
ReportData

<new>

Specify the sheet for cluster membership and distance between observations and clusters.

Distance Matrix

rddist

Output
ReportData

<new>

Specify the sheet for distance matrix when number of objects to cluster is very large. This variable is hidden in the dialog.

Plot Data

rdplot

Output
ReportData

<new>

Specify the sheet for plot data. This variable is hidden in the dialog.

Clustroid Info

clustroid

Input
int

1

Specify the method to find the Clustroid Info: the most/least representative variable/observation
