Print Email Facebook Twitter Developing efficient heuristic approaches to cluster editing, inspired by other clustering problems Title Developing efficient heuristic approaches to cluster editing, inspired by other clustering problems Author Zoumis, Angelos (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Software Technology; TU Delft Intelligent Systems) Contributor Demirović, E. (mentor) Pouwelse, J.A. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2021-07-01 Abstract Cluster editing attempts to find the minimum number of edge additions and removals on an undirected graph, that will transform the graph to one consisting of only disconnected cliques. In this paper, we propose three heuristic approaches to this problem, based on algorithms used to solve different clustering problems. The algorithms were based on agglomerative, divisive and k-means clustering algorithms. Experimental results show that all three algorithms are able to find results close to the minimum number of edits, but in particular, the k-means algorithm has a lower time complexity compared to the other two algorithms, while producing on average, the lowest number of edits. Subject Agglomerative hierarchical clusteringclosed neighborhoodCluster editingDivisive hierarchical clusteringK-means To reference this document use: http://resolver.tudelft.nl/uuid:70669928-f3a9-4756-87bf-d02d14efa412 Part of collection Student theses Document type bachelor thesis Rights © 2021 Angelos Zoumis Files PDF Research_paper.pdf 506.27 KB Close viewer /islandora/object/uuid:70669928-f3a9-4756-87bf-d02d14efa412/datastream/OBJ/view