Orange Workflows

Text Clustering

The workflow clusters Grimm’s tales corpus. We start by preprocessing the data and constructing the bag of words matrix. Then we compute cosine distances between documents and use Hierarchical Clustering, which displays the dendrogram. We observe how well the type of the tale corresponds to the cluster in the MDS.

Cluster Inspection

We use the zoo data set in combination with Hierarchical Clustering to discover groups of animals. Now that we have the clusters we want to find out what is significant for each cluster! Pass the clusters to Box Plot and use ‘Order by relevance’ to discover what defines a cluster. Seems like they are well-separated by the type, even though the clustering was unaware of the class label!

Hierarchical Clustering

The workflow clusters the data items in iris dataset by first examining the distances between data instances. Distance matrix is passed to Hierarchical Clustering, which renders the dendrogram. Select different parts of the dendrogram to further analyze the corresponding data.