The fuzzy c-means algorithm is a well-known unsupervised learning
technique that can be used to reveal the underlying structure of the data.
Fuzzy clustering allows each data point to belong to several clusters, with
a degree of membership to each one.
Make sure that the input data is normalized to obtain better clustering results.
The list of attributes to use can be set in the second tab of the dialog.
The first output datatable provides the original datatable with the cluster memberships to each cluster. The second datatable provides the values of the cluster prototypes.
Additionally, it is possible to induce a noise cluster, to detect noise in the dataset, based on the approach from R. N. Dave: 'Characterization and detection of noise in clustering'.
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.
A zipped version of the software site can be downloaded here.
Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.Try NodePit Runner!
Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to firstname.lastname@example.org, follow @NodePit on Twitter, or chat on Gitter!
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.