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 language | English (US) |
|---|---|
| Title of host publication | Mining Text Data |
| Publisher | Springer US |
| Pages | 77-128 |
| Number of pages | 52 |
| Volume | 9781461432234 |
| ISBN (Electronic) | 9781461432234 |
| ISBN (Print) | 1461432227, 9781461432227 |
| DOIs | |
| State | Published - Aug 1 2012 |
Fingerprint
Keywords
- Text clustering
ASJC Scopus subject areas
- Computer Science(all)
Cite this
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 proceeding › Chapter
}
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 -