This function generates dendrogram of input data. it illustrates the arrangement of the clusters produced by the corresponding analyses.
AXCEL.VIZ.DENDROGRAM(data, [orientation], [method], [title], [deployment])
The AXCEL.VIZ.DENDROGRAM function syntax has the following arguments:
data Required. data must have at least two columns. The first column is considered as the final classification names. If this column has duplicates, Axcel automatically index them to make them all unique variables. The second and following columns are used for clustering calculation.
|Cluster_names||value1||value2 (optional)||…||x_value_n (optional)|
orientation Optional. There are four orientation types: [H]orizontal, [V]ertical, [D]iagonal and [R]adial. Default is Horizontal.
method Optional. The agglomeration method used in the hierarchical clustering. Here is the list of available methods:
[Si]ngle [Co]mplete (Default) [Av]erage [Mc]quity [Me]dian [Ce]ntroid
title Optional. By default, Axcel tries to find the title from your data such as the name of the first column. Otherwise, you can explicitly define the title of your graph.
deployment Optional. It is the deployment in project/name or owner/project/name format. You need to create a project by logging into your console (https://console.axcel.io) -> Project -> Create Project. After that you can use the project name in your deployment. Please note project and visualization names contain small letters and numbers only. If a project is shared with you, you should use the username of the owner in your deployment. Please visit visualization projects and sharing to learn more about this powerful feature.
when you type =AXCEL.VIZ.DENDROGRAM in an Excel cell, the IntelliSense guides you through required and optional (shown in  brackets) inputs. Here are examples.
In this example, we use USArrests dataset available in Axcel datasets which is the number of arrests per 100,000 population for different crimes in 50 states. To do so, in Cell A1 run:
In our example, we use first 20 states of the datasets. So let’s run:
which produces this plot (for better presentation we show the expanded plots inside the browser):
We can change the methodology to “average”:
which generates an output different from “complete” method presented before:
Now, let’s try different orientation. Starting with vertical:
which shows this graph:
Here is the command for diagonal:
which results in:
and lastly, the radial style:
which shows as follows:
See also Visualization Projects and Sharing