An incremental approach for solution of text clustering problem
by Burak Ordin; Duygu Selin Ballı; Nur Uylaş Satı
International Journal of Knowledge Engineering and Data Mining (IJKEDM), Vol. 6, No. 3, 2019

Abstract: Text clustering is a significant study field in data mining. In text clustering, hidden-information is revealed by analysing text datasets with data mining techniques. Text clustering is used in many current application areas such as document recognition, document organisation, indexing, and visualisation. Since it is used in important applications it is in need of efficient algorithms. In this paper, k-means based algorithms in literature are investigated and a novel k-means based text clustering algorithm is defined. Proposed algorithm is compared with existing k-means algorithm in literature on five real-world datasets. Obtained implications show that recommended algorithm is efficient and useful.

Online publication date: Fri, 27-Sep-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Knowledge Engineering and Data Mining (IJKEDM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com