Clustering Algorithms For Intelligent Web
2016 (English)In: International Journal of Computational Complexity and Intelligent Algorithmslgorithms, ISSN 2048-4720Article in journal (Refereed) In press
Detecting users and data in the web is an important issue as the web is changing and new information is created every day. In this paper we will discuss six different clustering algorithms that are related to the intelligent web. These algorithms will help us to identify groups of interest in the web, which is very necessary in or- der to perform certain actions on specific group such as targeted advertisement. The algorithms under consideration are: Single-Link algorithm, Average-Link algorithm, Minimum-Spanning-Tree Single-Link algorithm, K-means algorithm, ROCK algorithm and DBSCAN algorithm. These algorithms are categorized into three groups: Hierarchical, Partitional and Density-based algorithms. We will show how each algorithm works and discuss their advantages and disadvantages. We will compare these algorithms to each others and discuss their ability to handle social web data which are of large datasets and high dimensionality. Finally a case study related to using clustering in social networks will be discussed.
Place, publisher, year, edition, pages
InderScience Publishers, 2016.
Algorithms, clustering, intelligent web, social networks
Computer and Information Science
IdentifiersURN: urn:nbn:se:his:diva-12067OAI: oai:DiVA.org:his-12067DiVA: diva2:913716