corpusClustDlg: Hierarchical clustering of a tm corpus

Description Details See Also


Hierarchical clustering of the documents of a tm corpus.


This dialog allows creating a tree of the documents present in a tm corpus either based on its document-term matrix, or on selected dimensions of a previously run correspondence analysis (if no correspondence analysis has been performed, the relevant widgets are not available). With both methods, the dendrogram starts with all separate documents at the bottom, and progressively merges them into clusters until reaching a single group at the top.

Technically, Ward's minimum variance method is used with a Chi-squared distance: see hclust for details about the clustering process.

The first slider allows skipping less significant terms to use less memory with large corpora. The second allows choosing what dimensions of the correspondence analysis should be used, which helps removing noise to concentrate on identified caracteristics of the corpus.

Since the clustering by itself only returns a tree, cutting it at a given size is needed to create classes of documents: this is offered automatically after the dendrogram has been computed, and can be achieved as many times as needed thanks to the Text Mining->Hierarchical clustering->Create clusters... dialog.

See Also

hclust, dist, corpusCaDlg, removeSparseTerms, DocumentTermMatrix, createClustersDlg

RcmdrPlugin.temis documentation built on May 2, 2019, 11:10 a.m.