Clusters Identification: Asymmetrical Case
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Cluster analysis is one of the typical tasks in Data Mining, and it groups data objects based only on information found in the data that describes the objects and their relationships. The purpose of this thesis is to verify a modified K-means algorithm in asymmetrical cases, which can be regarded as an extension to the research of Vladislav Valkovsky and Mikael Karlsson in Department of Informatics and Media. In this thesis an experiment is designed and implemented to identify clusters with the modified algorithm in asymmetrical cases. In the experiment the developed Java application is based on knowledge established from previous research. The development procedures are also described and input parameters are mentioned along with the analysis. This experiment consists of several test suites, each of which simulates the situation existing in real world, and test results are displayed graphically. The findings mainly emphasize the limitations of the algorithm, and future work for digging more essences of the algorithm is also suggested.
Place, publisher, year, edition, pages
2013. , 65 p.
Modified K-means algorithm, Nearest neighbor clustering, Kolmogorov-Smirnov-test, Hypothesis testing
Social Sciences Information Systems, Social aspects
IdentifiersURN: urn:nbn:se:uu:diva-208328OAI: oai:DiVA.org:uu-208328DiVA: diva2:651983
Subject / course
Master programme in Information Systems
2013-09-13, A311, Ekonomikum (plan 3), Kyrkogårdsg. 10, Uppsala, 15:00 (English)
Valkovsky, Vladislav, Associate Professor
McKeever, Steve, Senior lecturer