{"main_file_link":[{"url":"https://link.springer.com/chapter/10.1007/978-3-642-16111-7_21"}],"status":"public","keyword":["Markov Chain","Cluster Algorithm","Edge Weight","Spectral Cluster","Stable Distribution"],"citation":{"ufg":"<b>Niggemann, Oliver et. al. (2010)</b>: A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs, in: <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i> (=<i>Lecture Notes in Computer Science 6359</i>), Berlin, S. 184–194.","apa":"Niggemann, O., Lohweg, V., &#38; Tack, T. (2010). A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs. In <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i> (Vol. 6359, pp. 184–194). Berlin: Springer. <a href=\"https://doi.org/10.1007/978-3-642-16111-7_21\">https://doi.org/10.1007/978-3-642-16111-7_21</a>","van":"Niggemann O, Lohweg V, Tack T. A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs. In: 33rd Annual German Conference on Artificial Intelligence (KI 2010). Berlin: Springer; 2010. p. 184–94. (Lecture Notes in Computer Science; vol. 6359).","bjps":"<b>Niggemann O, Lohweg V and Tack T</b> (2010) A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs. <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i>, vol. 6359. Berlin: Springer, pp. 184–194.","short":"O. Niggemann, V. Lohweg, T. Tack, in: 33rd Annual German Conference on Artificial Intelligence (KI 2010), Springer, Berlin, 2010, pp. 184–194.","chicago":"Niggemann, Oliver, Volker Lohweg, and Tim Tack. “A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs.” In <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i>, 6359:184–94. Lecture Notes in Computer Science. Berlin: Springer, 2010. <a href=\"https://doi.org/10.1007/978-3-642-16111-7_21\">https://doi.org/10.1007/978-3-642-16111-7_21</a>.","havard":"O. Niggemann, V. Lohweg, T. Tack, A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs, in: 33rd Annual German Conference on Artificial Intelligence (KI 2010), Springer, Berlin, 2010: pp. 184–194.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Niggemann, Oliver</span> ; <span style=\"font-variant:small-caps;\">Lohweg, Volker</span> ; <span style=\"font-variant:small-caps;\">Tack, Tim</span>: A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs. In: <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i>, <i>Lecture Notes in Computer Science</i>. Bd. 6359. Berlin : Springer, 2010, S. 184–194","ama":"Niggemann O, Lohweg V, Tack T. A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs. In: <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i>. Vol 6359. Lecture Notes in Computer Science. Berlin: Springer; 2010:184-194. doi:<a href=\"https://doi.org/10.1007/978-3-642-16111-7_21\">https://doi.org/10.1007/978-3-642-16111-7_21</a>","ieee":"O. Niggemann, V. Lohweg, and T. Tack, “A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs,” in <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i>, 2010, vol. 6359, pp. 184–194.","chicago-de":"Niggemann, Oliver, Volker Lohweg und Tim Tack. 2010. A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs. In: <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i>, 6359:184–194. Lecture Notes in Computer Science. Berlin: Springer. doi:<a href=\"https://doi.org/10.1007/978-3-642-16111-7_21,\">https://doi.org/10.1007/978-3-642-16111-7_21,</a> .","mla":"Niggemann, Oliver, et al. “A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs.” <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i>, vol. 6359, Springer, 2010, pp. 184–94, doi:<a href=\"https://doi.org/10.1007/978-3-642-16111-7_21\">https://doi.org/10.1007/978-3-642-16111-7_21</a>."},"type":"conference","place":"Berlin","language":[{"iso":"eng"}],"title":"A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs","date_created":"2019-12-02T08:21:26Z","date_updated":"2023-03-15T13:49:38Z","_id":"2088","year":2010,"intvolume":"      6359","publication_status":"published","author":[{"first_name":"Oliver","last_name":"Niggemann","full_name":"Niggemann, Oliver","id":"10876"},{"last_name":"Lohweg","first_name":"Volker","id":"1804","orcid":"0000-0002-3325-7887","full_name":"Lohweg, Volker"},{"full_name":"Tack, Tim","first_name":"Tim","last_name":"Tack"}],"doi":"https://doi.org/10.1007/978-3-642-16111-7_21","page":"184-194","publisher":"Springer","department":[{"_id":"DEP5023"}],"user_id":"45673","volume":6359,"series_title":"Lecture Notes in Computer Science","abstract":[{"lang":"eng","text":"Clustering remains a major topic in machine learning; it is used e.g. for document categorization, for data mining, and for image analysis. In all these application areas, clustering algorithms try to identify groups of related data in large data sets.\r\n\r\nIn this paper, the established clustering algorithm MajorClust ([12]) is improved; making it applicable to data sets with few structure on the local scale—so called near-homogeneous graphs. This new algorithm MCProb is verified empirically using the problem of image clustering. Furthermore, MCProb is analyzed theoretically. For the applications examined so-far, MCProb outperforms other established clustering techniques."}],"publication":"33rd Annual German Conference on Artificial Intelligence (KI 2010)","publication_identifier":{"eisbn":["978-3-642-16111-7"],"isbn":["978-3-642-16110-0"]}}