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Auflistung nach Autor:in "Beer, Anna"

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  • Zeitschriftenartikel
    Chain-detection Between Clusters
    (Datenbank-Spektrum: Vol. 19, No. 3, 2019) Held, Janis; Beer, Anna; Seidl, Thomas
    Chains connecting two or more different clusters are a well known problem of clustering algorithms like DBSCAN or Single Linkage Clustering. Since already a small number of points resulting from, e. g., noise can form such a chain and build a bridge between different clusters, it can happen that the results of the clustering algorithm are distorted: several disparate clusters get merged into one. This single-link effect is rather known but to the best of our knowledge there are no satisfying solutions which extract those chains, yet. We present a new algorithm detecting not only straight chains between clusters, but also bent and noisy ones. Users are able to choose between eliminating one dimensional and higher dimensional chains connecting clusters to receive the underlying cluster structure. Also, the desired straightness can be set by the user. As this paper is an extension of [ 8 ], we apply our technique not only in combination with DBSCAN but also with single link hierarchical clustering. On a real world dataset containing traffic accidents in Great Britain we were able to detect chains emerging from streets between cities and villages, which led to clusters composed of diverse villages. Additionally, we analyzed the robustness regarding the variance of chains in synthetic experiments.
  • Textdokument
    Chain-detection for DBSCAN
    (BTW 2019 – Workshopband, 2019) Held, Janis; Beer, Anna; Seidl, Thomas
    Chains connecting two or more different clusters are a well known problem of the probably most famous density-based clustering algorithm DBSCAN. Since already a small number of points resulting from, e.g., noise can form such a chain and build a bridge between different clusters, it can happen that the results of DBSCAN are distorted: several disparate clusters get merged into one. This single-link effect is rather known but to the best of our knowledge there are no satisfying solutions which extract those chains, yet. We present a new algorithm detecting not only straight chains between clusters, but also bent and noisy ones. Users are able to choose between eliminating one dimensional and higher dimensional chains connecting clusters to receive the underlying cluster structure by DBSCAN. Also, the desired straightness can be set by the user. We tested our efficient algorithm on a dataset containing traffic accidents in Great Britain and were able to detect chains emerging from streets between cities and villages, which led to clusters composed of diverse villages.
  • Textdokument
    Cluster Flow - an Advanced Concept for Ensemble-Enabling, Interactive Clustering
    (BTW 2021, 2021) Obermeier, Sandra; Beer, Anna; Wahl, Florian; Seidl, Thomas
    Even though most clustering algorithms serve knowledge discovery in fields other than computer science, most of them still require users to be familiar with programming or data mining to some extent. As that often prevents efficient research, we developed an easy to use, highly explainable clustering method accompanied by an interactive tool for clustering. It is based on intuitively understandable kNN graphs and the subsequent application of adaptable filters, which can be combined ensemble-like and iteratively and prune unnecessary or misleading edges. For a first overview of the data, fully automatic predefined filter cascades deliver robust results. A selection of simple filters and combination methods that can be chosen interactively yield very good results on benchmark datasets compared to various algorithms.