Publication Type:

Journal Article

Source:

International Journal of Control Theory and Applications, Serials Publications, Volume 9, Number 15, p.7619-7626 (2016)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85005938269&partnerID=40&md5=1bda56144948842386b095df0b221985

Abstract:

Text Mining is remarkably a new and stimulating research area in this modern world of technological era. Text clustering is a text mining technique. It groups a set of objects in such a way that, objects in the same group (called a cluster) are more similar in one way or the other to each other than to those in other groups or clusters. There are several techniques to accomplish text clustering. Initially we did clustering using the K-means algorithm. It was found that text clustering requires text data that must be converted into numerical to get more accurate results. The paper concentrates on K-means algorithm. It's the fastest algorithm and can deal with large data sets efficiently. However, k-means encounter certain problems while dealing with text data. The paper focuses on eliminating this drawback by directly converting the text data into a numeric value which results in more defined clusters and accurate running time.

Notes:

cited By 0

Cite this Research Publication

S. Unnikrishnan, Sreelakshmi, S., and Deepa, G., “Enhancement of accuracy in K-means clustering”, International Journal of Control Theory and Applications, vol. 9, pp. 7619-7626, 2016.

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