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K-Means Cluster - Python Data Function for Spotfire® 1.0.0

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This Python data function implements K-means clustering algorithm which tries to group similar items in the form of clusters. The number of groups is represented by K.


This Python data function helps in grouping data into two or more groups based on the properties of the data, and more exactly based on certain patterns which are more or less obvious in the data. The goal is to find those patterns in the data that help us be sure that, given a certain item in our dataset, we will be able to correctly place the item in a correct groups represented by K, so that it is similar to other items in that group, but different from items in other groups.

The ideal result for this data function is that two items in the same group are as similar to each other, while two items from different groups are as different as possible.

Also available as a Statistica data function.


Installing the data function

Follow the online guide available here to register a data function in Spotfire.


Configuring the data function

Each data function may require inputs from the Spotfire analysis and will return outputs to the Spotfire analysis. For each data function, these need to be configured once the data function is registered. To learn about how to configure data functions in Spotfire please view this video:

For more information on Spotfire visit the Spotfire training page.


Data function library

There exists a large number of data functions covering various features. Feel free to review what is available on the Data Function Library.

Release 1.0.0

Published: April 2021

Initial release includes:

  • Data function
  • Dxp with example usage
  • Documentation
  • License information

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