A methodology for Brand Name Hierarchical Clustering Based on Social Media Data. | [Journal of Applied and Emerging Sciences • 2018]

Author(s):

1. Tasweer Hussain Shah: Department of Computer Science and Information Technology, Virtual University of, Pakistan

2. Nasir Naveed: Department of Computer Science and Information Technology, Virtual University of, Pakistan

3. Zahid Rauf: Department of Electrical Engineering, Faculty of Information and Communication Technology, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, Pakistan

Abstract:

Clustering of brand names is a common technique for identification of different groups of brand names with maximum effectiveness and similarities. In this study, we proposed a methodology for brand name clustering, based on data from social media like Twitters. Dataset consist of tweets mentioning various garment brands in them. In order to cluster the brand names, we have proposed an algorithm, named BNACA (Brand Names Agglomerate Clustering Algorithm), an extension to the standard hierarchical clustering algorithm. In the proposed algorithm, we used single linkage as a similarity measure. The proposed clustering algorithm provides consistent clustered results for various sets of brand names of garment industry. Finally, clusters of garment brand names are visualized through dendogram. The dendogram clearly shows the maximum similarities among garment brand names.

Page(s): 10-23

DOI: DOI not available

Published: Journal: Journal of Applied and Emerging Sciences , Volume: 8, Issue: 1, Year: 2018

Keywords:

Keywords are not available for this article.

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