Frequent term-based text clustering

By Florian Beil, Martin Ester, Xiaowei Xu. Knowledge Discovery and Data Mining, 2002

648 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Efficient phrase-based document indexing for Web document clustering (2004, background): “Beil et al. [36] proposed a clustering algorithm based on frequent terms that address the high dimensionality problem of text data sets.”
Frequent pattern mining: current status and future directions (2007, methodology): “Beil et al. (2002) proposed a method for frequent term-based text clustering.”
Term Graph Model for Text Classification (2005, methodology): “We can find out several different methods and system for clustering transactions [16] and documents [2, 6].”
Feature Selection for Clustering: A Review (2018, methodology): “Frequent Term-Based Text Clustering (FTC) proposed in [8] provides a natural way to reduce dimensionality in text clustering.”
Hierarchical Document Clustering using Frequent Itemsets (2003, background): “A minimum cluster support , in a percentage of of the documents in C i , can be speciﬁed for this purpose.”