Partitioning nodes of a graph into clusters according to their simi- larities can be a very useful but complex task of data analysis. Many dierent approaches and algorithms for this problem exist, one of the possibilities is to utilize genetic algorithms for solving this type of task. In this work, we analyze dierent approaches to clustering in general and in the domain of graphs. Several clustering algorithms based on the concept of genetic algorithm are proposed and experimentally evaluated. A server application that contains implementations of the these algorithms was developed and is attached to this thesis.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:306021 |
| Date | January 2012 |
| Creators | Kohout, Jan |
| Contributors | Neruda, Roman, Mrázová, Iveta |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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