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A Comparative Study of Clustering Algorithms

A Comparative Study of Clustering Algorithms
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Author(s): Kanna Al Falahi (United Arab Emirates University-Al Ain, UAE), Saad Harous (United Arab Emirates University-Al Ain, UAE) and Yacine Atif (United Arab Emirates University-Al Ain, UAE)
Copyright: 2013
Pages: 17
Source title: Studies in Virtual Communities, Blogs, and Modern Social Networking: Measurements, Analysis, and Investigations
Source Author(s)/Editor(s): Subhasish Dasgupta (George Washington University, USA)
DOI: 10.4018/978-1-4666-4022-1.ch009


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Clustering is a major problem when dealing with organizing and dividing data. There are multiple algorithms proposed to handle this issue in many scientific areas such as classifications, community detection and collaborative filtering. The need for clustering arises in Social Networks where huge data generated daily and different relations are established between users. The ability to find groups of interest in a network can help in many aspects to provide different services such as targeted advertisements. The authors surveyed different clustering algorithms from three different clustering groups: Hierarchical, Partitional, and Density-based algorithms. They then discuss and compare these algorithms from social web point view and show their strength and weaknesses in handling social web data. They also use a case study to support our finding by applying two clustering algorithms on articles collected from and discussing the different groups generated by each algorithm.

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