Sentence examples for document cluster from inspiring English sources

Fig. 8 Mapper and reducer for LDA topic generation from document cluster.

LDA (Latent Dirichlet Allocation) technique is used in this work for generating topics from each document cluster.

The Mapper computes the semantic similar terms for each topic term generated by the document cluster and reducer aggregate these terms and counts the frequencies of these terms (topic terms and semantic similar terms of topic terms) aggregately.

In the second stage Latent Dirichlet Allocation (LDA) topic modeling technique is applied on each individual text document cluster to generate the cluster topics and terms belonging to each cluster topic.

Fig. 4 Document clustering using clustering algorithm.

Fig. 7 Mapper and reducer for document clustering.

Some examples of document similarity are document clustering, document categorization, document summarization, and query-based search.

The first stage is the document clustering stage where text clustering technique is applied on the multi document text collection to create the text document clusters.

In the semi-supervised document clustering, each cluster is represented by a language model.

Each latent variable gives a soft partition of the documents, and document clusters in the partitions are interpreted as topics.

These results can be directly used in document clustering and recommendation systems.