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 |
Keywords
- Text clustering
ASJC Scopus subject areas
- Computer Science(all)
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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