R: Represent a document clustering as a topic model

Cluster2TopicModel {textmineR} | R Documentation

## Represent a document clustering as a topic model

### Description

Represents a document clustering as a topic model of two matrices. phi: P(term | cluster) theta: P(cluster | document)

### Usage

    Cluster2TopicModel(dtm, clustering, ...)

### Arguments

`dtm` | A document term matrix of class `dgCMatrix` or whose class inherits from the `Matrix` package. Columns must index terms, rows must index documents.
`clustering` | A vector of length `nrow(dtm)` whose entries form a partitional clustering of the documents.
`...` | Other arguments to be passed to `TmParallelApply`.

### Value

Returns a list with two elements, phi and theta. 'phi' is a matrix whose j-th row represents P(terms | cluster_j). 'theta' is a matrix whose j-th row represents P(clusters | document_j). Each row of theta should only have one non-zero element.

### Examples

    ## Not run:
    # Load pre-formatted data for use
    data(nih_sample_dtm)
    data(nih_sample)

    result <- Cluster2TopicModel(dtm = nih_sample_dtm,
                                 clustering = nih_sample$IC_NAME)

    ## End(Not run)

[Package textmineR version 3.0.5 Index]