A survey of text clustering algorithms

Charu C. Aggarwal, Cheng Xiang Zhai

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the text domain. We will discuss the key methods used for text clustering, and their relative advantages. We will also discuss a number of recent advances in the area in the context of social network and linked data.

Original languageEnglish (US)
Title of host publicationMining Text Data
PublisherSpringer US
Pages77-128
Number of pages52
Volume9781461432234
ISBN (Electronic)9781461432234
ISBN (Print)1461432227, 9781461432227
DOIs
StatePublished - Aug 1 2012

Fingerprint

Collaborative filtering
Clustering algorithms
Data mining
Visualization

Keywords

  • Text clustering

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Aggarwal, C. C., & Zhai, C. X. (2012). A survey of text clustering algorithms. In Mining Text Data (Vol. 9781461432234, pp. 77-128). Springer US. https://doi.org/10.1007/978-1-4614-3223-4_4

A survey of text clustering algorithms. / Aggarwal, Charu C.; Zhai, Cheng Xiang.

Mining Text Data. Vol. 9781461432234 Springer US, 2012. p. 77-128.

Research output: Chapter in Book/Report/Conference proceedingChapter

Aggarwal, CC & Zhai, CX 2012, A survey of text clustering algorithms. in Mining Text Data. vol. 9781461432234, Springer US, pp. 77-128. https://doi.org/10.1007/978-1-4614-3223-4_4
Aggarwal CC, Zhai CX. A survey of text clustering algorithms. In Mining Text Data. Vol. 9781461432234. Springer US. 2012. p. 77-128 https://doi.org/10.1007/978-1-4614-3223-4_4
Aggarwal, Charu C. ; Zhai, Cheng Xiang. / A survey of text clustering algorithms. Mining Text Data. Vol. 9781461432234 Springer US, 2012. pp. 77-128
@inbook{960683f64513416d9a5cb1a518ff2ef8,
title = "A survey of text clustering algorithms",
abstract = "Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the text domain. We will discuss the key methods used for text clustering, and their relative advantages. We will also discuss a number of recent advances in the area in the context of social network and linked data.",
keywords = "Text clustering",
author = "Aggarwal, {Charu C.} and Zhai, {Cheng Xiang}",
year = "2012",
month = "8",
day = "1",
doi = "10.1007/978-1-4614-3223-4_4",
language = "English (US)",
isbn = "1461432227",
volume = "9781461432234",
pages = "77--128",
booktitle = "Mining Text Data",
publisher = "Springer US",

}

TY - CHAP

T1 - A survey of text clustering algorithms

AU - Aggarwal, Charu C.

AU - Zhai, Cheng Xiang

PY - 2012/8/1

Y1 - 2012/8/1

N2 - Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the text domain. We will discuss the key methods used for text clustering, and their relative advantages. We will also discuss a number of recent advances in the area in the context of social network and linked data.

AB - Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the text domain. We will discuss the key methods used for text clustering, and their relative advantages. We will also discuss a number of recent advances in the area in the context of social network and linked data.

KW - Text clustering

UR - http://www.scopus.com/inward/record.url?scp=84949179803&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84949179803&partnerID=8YFLogxK

U2 - 10.1007/978-1-4614-3223-4_4

DO - 10.1007/978-1-4614-3223-4_4

M3 - Chapter

AN - SCOPUS:84949179803

SN - 1461432227

SN - 9781461432227

VL - 9781461432234

SP - 77

EP - 128

BT - Mining Text Data

PB - Springer US

ER -