Sentence examples for text clusters from inspiring English sources

The topic terms generated for text clusters are taken as input to the summarizer which are shuffled and broadcasted to the mappers in Map-Reduce framework.

The mean (SD) fold difference in expression as measured by RT-PCR is calculated as iL3s/L1 for iL3-up (normal text) contigs and clusters and L1/iL3 for L1-up (bold text) clusters and contigs and for the reference genes.

After this operation, it preprocesses text document to perform pronominal reference resolution and text clustering.

There are three main challenges in applying text clustering to Arabic Web page content.

This paper explores the application of text clustering to natural language texts written during the Engineering Change process in industry.

The results show that SOM text clustering has a good potential to improve overall knowledge reuse and exploitation.

It may be applied to the short text classification, recommender system, and short text clustering.

Existing solutions commonly use text clustering, association rule mining, or latent semantic models for topic discovery.

Text classification and text clustering, as basic text mining tasks, are frequently applied in semantics-concerned text mining researches.

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.

Keyphrase extraction also provides useful resources for text clustering [8], text classification [35], and document summarization [32].