{"language":[{"iso":"eng"}],"abstract":[{"text":"We present H-NGPCA, a hierarchical clustering algorithm for data streams that integrates an adaptive unit number growth and local dimensionality control. Unlike existing algorithm, H-NGPCA combines the characteristics of centroid-based, model-based and hierarchical clustering. H-NGPCA builds a hierarchical structure of local Principal Component Analysis (PCA) units, where each unit is a hyper-ellipsoid whose shape is updated by a neural network-based online PCA method. The re-positioning of each unit is handled by Neural Gas, a centroid-based clustering algorithm. In the hierarchical tree structure, new units are created in a branch if suggested by a splitting criterion. In addition, each unit determines its own dimensionality based on the data represented by the unit. In extensive benchmarks, H-NGPCA not only surpasses all competing online algorithms with adaptive unit numbers but also achieves competitive performance with state-of-the-art offline methods, reaching an average NMI = 0.87 and CI = 0.26. This demonstrates that H-NGPCA achieves both online adaptability and offline-level accuracy.\r\n                ","lang":"eng"}],"title":"H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality","_id":"6485","citation":{"ama":"Migenda N, Möller R, Schenck W. H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality. <i>PLOS One</i>. 2026;21(1). doi:<a href=\"https://doi.org/10.1371/journal.pone.0339171\">10.1371/journal.pone.0339171</a>","apa":"Migenda, N., Möller, R., &#38; Schenck, W. (2026). H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality. <i>PLOS One</i>, <i>21</i>(1). <a href=\"https://doi.org/10.1371/journal.pone.0339171\">https://doi.org/10.1371/journal.pone.0339171</a>","alphadin":"<span style=\"font-variant:small-caps;\">Migenda, Nico</span> ; <span style=\"font-variant:small-caps;\">Möller, Ralf</span> ; <span style=\"font-variant:small-caps;\">Schenck, Wolfram</span>: H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality. In: <i>PLOS One</i> Bd. 21, Public Library of Science (PLoS) (2026), Nr. 1","ieee":"N. Migenda, R. Möller, and W. Schenck, “H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality,” <i>PLOS One</i>, vol. 21, no. 1, 2026.","mla":"Migenda, Nico, et al. “H-NGPCA: Hierarchical Clustering of Data Streams with Adaptive Number of Clusters and Adaptive Dimensionality.” <i>PLOS One</i>, vol. 21, no. 1, e0339171, Public Library of Science (PLoS), 2026, doi:<a href=\"https://doi.org/10.1371/journal.pone.0339171\">10.1371/journal.pone.0339171</a>.","chicago":"Migenda, Nico, Ralf Möller, and Wolfram Schenck. “H-NGPCA: Hierarchical Clustering of Data Streams with Adaptive Number of Clusters and Adaptive Dimensionality.” <i>PLOS One</i> 21, no. 1 (2026). <a href=\"https://doi.org/10.1371/journal.pone.0339171\">https://doi.org/10.1371/journal.pone.0339171</a>.","short":"N. Migenda, R. Möller, W. Schenck, PLOS One 21 (2026).","bibtex":"@article{Migenda_Möller_Schenck_2026, title={H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality}, volume={21}, DOI={<a href=\"https://doi.org/10.1371/journal.pone.0339171\">10.1371/journal.pone.0339171</a>}, number={1e0339171}, journal={PLOS One}, publisher={Public Library of Science (PLoS)}, author={Migenda, Nico and Möller, Ralf and Schenck, Wolfram}, year={2026} }"},"issue":"1","doi":"10.1371/journal.pone.0339171","funded_apc":"1","publication_status":"published","intvolume":"        21","user_id":"224375","oa":"1","date_created":"2026-01-28T06:40:15Z","author":[{"orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-7223-1735/work/203835768","first_name":"Nico","last_name":"Migenda","orcid":"0000-0002-7223-1735","id":"218473","full_name":"Migenda, Nico"},{"full_name":"Möller, Ralf","last_name":"Möller","first_name":"Ralf"},{"last_name":"Schenck","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-3300-2048/work/203835770","first_name":"Wolfram","orcid":"0000-0003-3300-2048","id":"224375","full_name":"Schenck, Wolfram"}],"type":"journal_article","article_type":"original","date_updated":"2026-01-29T14:09:29Z","publication_identifier":{"eissn":["1932-6203"]},"status":"public","article_number":"e0339171","publisher":"Public Library of Science (PLoS)","publication":"PLOS One","volume":21,"quality_controlled":"1","year":"2026","main_file_link":[{"open_access":"1"}]}