A Comparative Study of Clustering Algorithms

Research output: Chapter in Book/Report/Conference proceedingChapter


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 Delicious. com and discussing the different groups generated by each algorithm.

Original languageEnglish
Title of host publicationStudies in Virtual Communities, Blogs, and Modern Social Networking
Subtitle of host publicationMeasurements, Analysis, and Investigations
PublisherIGI Global
Number of pages17
ISBN (Electronic)9781466640238
ISBN (Print)1466640227, 9781466640221
Publication statusPublished - May 31 2013

ASJC Scopus subject areas

  • General Engineering


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