Stability of Hierarchical ClusteringTuranjanin, Aleksandra (Avtor) Zupan, Blaž (Mentor) hierarchical clusteringstabilitydendrogramunsupervised learningHierarchical clustering is an unsupervised data mining technique that infers a set of nested, hierarchically organised clusters. Even slight permutations in the data can change the clustering structure. Ideally, we should only be interested in the stable part of the clustering hierarchy. It is thus essential to assess the stability of the nodes in the hierarchy. In this thesis, we review the approaches to determine the stability and statistical significance of the clusters. While all the reviewed methods use resampling, their results could be substantially different because of the details in the implementation and stability scoring. The approach called pvclust is recently most used in practical applications. In its R implementation, it suffers from low speed and visualisation of results. We have implemented pvclust in Python, yielding an implementation that is almost an order of magnitude faster than the version in R. Our implementation is currently the only opensource Python implementation of stability analysis of hierarchical clustering. To visualise the results and enable interactive explorative data analysis, we also incorporated our implementation in the Orange data mining toolbox.20202020-09-29 10:40:01Magistrsko delo/naloga121038sl