[{"type":"industry_journal_article","publisher":"FFO","related_material":{"link":[{"relation":"confirmation","url":"https://www.researchgate.net/publication/387485714_L'orchidee_tridentee_Neotinea_tridentata"}]},"date_updated":"2025-01-07T07:47:04Z","department":[{"_id":"DEP1310"}],"year":"2024","user_id":"83781","status":"public","keyword":["Orchidaceae","Neotinea tridentata","orchidée  de l’année 2019","caractéristiques","distribution","cycle de vie","gestion","menaces","conservation","statut de protection"],"page":"139-148","abstract":[{"lang":"eng","text":"The Toothed Orchid,  Neotinea  tridentata, has been elected for Orchid of the Year 2019 by the steering committees of the German Native  Orchids  Associations  (Arbeitskreise Heimische Orchideen Deutschlands, AHO) Etymology, characteristics and life cycle as well as ecology and habitat of the species are specified. The range of the species is described and a distribution map is presented for Germany. Threats are discussed and methods of conservation management of the species are pointed out. Finally possible consequences of climate change for the species are discussed"},{"text":"L’orchis tridenté, Neotinea tridentata, a été élue orchidée de l‘année 2019 par les comités directeurs des associations allemandes d‘orchidées indigènes (Arbeitskreise Heimische Orchideen  Deutschlands,  AHO)  L‘étymologie, les caractéristiques et le cycle de vie ainsi que l‘écologie et l‘habitat de cette espèce sont spécifiés. L‘aire de répartition du taxon est décrite et une carte de répartition est présentée pour l‘Allemagne. Les menaces sont discutées et les méthodes de gestion de la conservation de l‘espèce sont soulignées. Enfin, les consé quences  possibles  du  changement  climatique pour l‘espèce sont abordées.","lang":"fre"}],"publication_identifier":{"issn":["2498-3713","0750-0386"]},"ddc":["570"],"intvolume":"       241","author":[{"first_name":"Mathias","full_name":"Lohr, Mathias","last_name":"Lohr","id":"22021"},{"first_name":"Jutta","full_name":"Haas, Jutta","last_name":"Haas"}],"language":[{"iso":"eng"}],"date_created":"2025-01-02T17:11:53Z","volume":241,"publication_status":"published","citation":{"ieee":"M. Lohr and J. Haas, “L’orchidée tridentée, Neotinea tridentata. ,” <i>L’Orchidophile</i>, vol. 241, pp. 139–148, 2024.","havard":"M. Lohr, J. Haas, L’orchidée tridentée, Neotinea tridentata. , L’Orchidophile. 241 (2024) 139–148.","van":"Lohr M, Haas J. L’orchidée tridentée, Neotinea tridentata. . L’Orchidophile. 2024;241:139–48.","ama":"Lohr M, Haas J. L’orchidée tridentée, Neotinea tridentata. . <i>L’Orchidophile</i>. 2024;241:139-148.","short":"M. Lohr, J. Haas, L’Orchidophile 241 (2024) 139–148.","bjps":"<b>Lohr M and Haas J</b> (2024) L’orchidée Tridentée, Neotinea Tridentata. . <i>L’Orchidophile</i> <b>241</b>, 139–148.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Lohr, Mathias</span> ; <span style=\"font-variant:small-caps;\">Haas, Jutta</span>: L’orchidée tridentée, Neotinea tridentata. . In: <i>L’Orchidophile</i> Bd. 241, FFO (2024), S. 139–148","mla":"Lohr, Mathias, and Jutta Haas. “L’orchidée Tridentée, Neotinea Tridentata. .” <i>L’Orchidophile</i>, vol. 241, 2024, pp. 139–48.","apa":"Lohr, M., &#38; Haas, J. (2024). L’orchidée tridentée, Neotinea tridentata. . <i>L’Orchidophile</i>, <i>241</i>, 139–148.","ufg":"<b>Lohr, Mathias/Haas, Jutta</b>: L’orchidée tridentée, Neotinea tridentata. , in: <i>L’Orchidophile</i> 241 (2024),  S. 139–148.","chicago-de":"Lohr, Mathias und Jutta Haas. 2024. L’orchidée tridentée, Neotinea tridentata. . <i>L’Orchidophile</i> 241: 139–148.","chicago":"Lohr, Mathias, and Jutta Haas. “L’orchidée Tridentée, Neotinea Tridentata. .” <i>L’Orchidophile</i> 241 (2024): 139–48."},"has_accepted_license":"1","title":"L’orchidée tridentée, Neotinea tridentata. ","publication":"L’Orchidophile","_id":"12274"},{"oa":"1","_id":"10590","publication":"Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg ","has_accepted_license":"1","title":"Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee  des Jahres 2021 ","main_file_link":[{"open_access":"1","url":"https://www.researchgate.net/publication/377571933_Das_Kriechende_Netzblatt_Goodyera_repens_L_R_Brown_-_Orchidee_des_Jahres_2021"}],"publication_identifier":{"issn":["0945-7909"]},"ddc":["580"],"intvolume":"        54","author":[{"first_name":"Marco","last_name":"Klüber","full_name":"Klüber, Marco"},{"id":"22021","last_name":"Lohr","full_name":"Lohr, Mathias","first_name":"Mathias"}],"publication_status":"published","volume":54,"language":[{"iso":"ger"}],"date_created":"2023-10-25T16:32:42Z","citation":{"van":"Klüber M, Lohr M. Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee  des Jahres 2021 . Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg . 2022;54(1–2):3–27.","ama":"Klüber M, Lohr M. Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee  des Jahres 2021 . <i>Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg </i>. 2022;54(1-2):3-27.","ieee":"M. Klüber and M. Lohr, “Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee  des Jahres 2021 ,” <i>Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg </i>, vol. 54, no. 1–2, pp. 3–27, 2022.","havard":"M. Klüber, M. Lohr, Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee  des Jahres 2021 , Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg . 54 (2022) 3–27.","chicago":"Klüber, Marco, and Mathias Lohr. “Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee  des Jahres 2021 .” <i>Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg </i> 54, no. 1–2 (2022): 3–27.","chicago-de":"Klüber, Marco und Mathias Lohr. 2022. Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee  des Jahres 2021 . <i>Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg </i> 54, Nr. 1–2: 3–27.","ufg":"<b>Klüber, Marco/Lohr, Mathias</b>: Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee  des Jahres 2021 , in: <i>Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg </i> 54 (2022), H. 1–2,  S. 3–27.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Klüber, Marco</span> ; <span style=\"font-variant:small-caps;\">Lohr, Mathias</span>: Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee  des Jahres 2021 . In: <i>Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg </i> Bd. 54. Stuttgart, AHO (2022), Nr. 1–2, S. 3–27","mla":"Klüber, Marco, and Mathias Lohr. “Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee  des Jahres 2021 .” <i>Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg </i>, vol. 54, no. 1–2, 2022, pp. 3–27.","apa":"Klüber, M., &#38; Lohr, M. (2022). Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee  des Jahres 2021 . <i>Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg </i>, <i>54</i>(1–2), 3–27.","bjps":"<b>Klüber M and Lohr M</b> (2022) Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee  des Jahres 2021 . <i>Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg </i> <b>54</b>, 3–27.","short":"M. Klüber, M. Lohr, Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg  54 (2022) 3–27."},"place":"Stuttgart","keyword":["Orchidaceae","Goodyera repens","Orchid of the Year 2021","characteristics","distribution","life history","monitoring","threats","conservation status","protection","Germany."],"page":"3-27","abstract":[{"lang":"ger","text":"Das Kriechende Netzblatt, Goodyera repens, das von den Vorständen der Arbeitskreise Heimische Orchideen Deutschlands (AHOs) zur Orchidee des Jahres 2021 gewählt wurde, wird vorgestellt. Namensgebung, morphologische Merkmale und Lebenszyklus sowie Ökologie und Lebensräume werden charakterisiert. Das Areal der Art wird beschrieben und die Verbreitung für Deutschland kartographisch dargestellt. Gefährdungsursachen werden erörtert und Schutzmaßnahmen aufgezeigt. Abschließend werden signifikante Konsequenzen des Klimawandels für die Art diskutiert und in Beispielen dargelegt."}],"department":[{"_id":"DEP9012"},{"_id":"DEP1310"}],"year":"2022","user_id":"83781","status":"public","issue":"1-2","date_updated":"2024-04-02T12:38:21Z","type":"industry_journal_article","publisher":"AHO"},{"article_number":"107765","_id":"5419","publication":"Biochemical Engineering Journal ","title":"Approach for modelling the extract formation in continuous conducted \"beta-amylase rest\" as part of the production of beer mash with targeted sugar content","publication_status":"published","volume":164,"language":[{"iso":"eng"}],"date_created":"2021-04-08T05:59:08Z","citation":{"van":"Wefing P, Conradi F, Trilling-Haasler M, Neubauer P, Schneider J. Approach for modelling the extract formation in continuous conducted “beta-amylase rest” as part of the production of beer mash with targeted sugar content. Biochemical Engineering Journal . 2020;164.","ama":"Wefing P, Conradi F, Trilling-Haasler M, Neubauer P, Schneider J. Approach for modelling the extract formation in continuous conducted “beta-amylase rest” as part of the production of beer mash with targeted sugar content. <i>Biochemical Engineering Journal </i>. 2020;164. doi:<a href=\"https://doi.org/10.1016/j.bej.2020.107765\">10.1016/j.bej.2020.107765</a>","ieee":"P. Wefing, F. Conradi, M. Trilling-Haasler, P. Neubauer, and J. Schneider, “Approach for modelling the extract formation in continuous conducted ‘beta-amylase rest’ as part of the production of beer mash with targeted sugar content,” <i>Biochemical Engineering Journal </i>, vol. 164, Art. no. 107765, 2020, doi: <a href=\"https://doi.org/10.1016/j.bej.2020.107765\">10.1016/j.bej.2020.107765</a>.","chicago":"Wefing, Patrick, Florian Conradi, Marc Trilling-Haasler, Peter Neubauer, and Jan Schneider. “Approach for Modelling the Extract Formation in Continuous Conducted ‘Beta-Amylase Rest’ as Part of the Production of Beer Mash with Targeted Sugar Content.” <i>Biochemical Engineering Journal </i> 164 (2020). <a href=\"https://doi.org/10.1016/j.bej.2020.107765\">https://doi.org/10.1016/j.bej.2020.107765</a>.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Wefing, Patrick</span> ; <span style=\"font-variant:small-caps;\">Conradi, Florian</span> ; <span style=\"font-variant:small-caps;\">Trilling-Haasler, Marc</span> ; <span style=\"font-variant:small-caps;\">Neubauer, Peter</span> ; <span style=\"font-variant:small-caps;\">Schneider, Jan</span>: Approach for modelling the extract formation in continuous conducted „beta-amylase rest“ as part of the production of beer mash with targeted sugar content. In: <i>Biochemical Engineering Journal </i> Bd. 164 (2020)","bjps":"<b>Wefing P <i>et al.</i></b> (2020) Approach for Modelling the Extract Formation in Continuous Conducted ‘Beta-Amylase Rest’ as Part of the Production of Beer Mash with Targeted Sugar Content. <i>Biochemical Engineering Journal </i> <b>164</b>.","havard":"P. Wefing, F. Conradi, M. Trilling-Haasler, P. Neubauer, J. Schneider, Approach for modelling the extract formation in continuous conducted “beta-amylase rest” as part of the production of beer mash with targeted sugar content, Biochemical Engineering Journal . 164 (2020).","chicago-de":"Wefing, Patrick, Florian Conradi, Marc Trilling-Haasler, Peter Neubauer und Jan Schneider. 2020. Approach for modelling the extract formation in continuous conducted „beta-amylase rest“ as part of the production of beer mash with targeted sugar content. <i>Biochemical Engineering Journal </i> 164. doi:<a href=\"https://doi.org/10.1016/j.bej.2020.107765\">10.1016/j.bej.2020.107765</a>, .","ufg":"<b>Wefing, Patrick u. a.</b>: Approach for modelling the extract formation in continuous conducted „beta-amylase rest“ as part of the production of beer mash with targeted sugar content, in: <i>Biochemical Engineering Journal </i> 164 (2020).","apa":"Wefing, P., Conradi, F., Trilling-Haasler, M., Neubauer, P., &#38; Schneider, J. (2020). Approach for modelling the extract formation in continuous conducted “beta-amylase rest” as part of the production of beer mash with targeted sugar content. <i>Biochemical Engineering Journal </i>, <i>164</i>, Article 107765. <a href=\"https://doi.org/10.1016/j.bej.2020.107765\">https://doi.org/10.1016/j.bej.2020.107765</a>","mla":"Wefing, Patrick, et al. “Approach for Modelling the Extract Formation in Continuous Conducted ‘Beta-Amylase Rest’ as Part of the Production of Beer Mash with Targeted Sugar Content.” <i>Biochemical Engineering Journal </i>, vol. 164, 107765, 2020, <a href=\"https://doi.org/10.1016/j.bej.2020.107765\">https://doi.org/10.1016/j.bej.2020.107765</a>.","short":"P. Wefing, F. Conradi, M. Trilling-Haasler, P. Neubauer, J. Schneider, Biochemical Engineering Journal  164 (2020)."},"intvolume":"       164","author":[{"last_name":"Wefing","id":"68976","full_name":"Wefing, Patrick","first_name":"Patrick"},{"full_name":"Conradi, Florian","last_name":"Conradi","id":"68967","first_name":"Florian"},{"orcid":"0000-0002-3685-6383","full_name":"Trilling-Haasler, Marc","last_name":"Trilling-Haasler","id":"81622","first_name":"Marc"},{"first_name":"Peter","last_name":"Neubauer","full_name":"Neubauer, Peter"},{"full_name":"Schneider, Jan","id":"13209","last_name":"Schneider","first_name":"Jan","orcid":"0000-0001-6401-8873"}],"abstract":[{"text":"Continuous mashing provides advantages compared to conventional batch-wise mashing in terms of space time yield. The majority of fermentable sugars are generated during the so-called “β-amylase rest” (62–64 ◦C). These low molecular sugars are fermented later in the brewing process by yeasts and therefore determine the beer attenuation degree. Biological malt variations complicate the application of a continuous system in industrial scale particularly concerning targeted quality parameters. The aim is the prediction of sugar formation from process parameters for a real time control system. Therefore, a semi-empirical model for sugar formation in a continuous stirred tank reactor (CSTR) system was developed under incorporation of the residence time distri- bution (RTD). The here presented model, which focuses on the “β-amylase rest”, is able to predict fermentable sugar concentrations in the continuous “β-amylase rest” with sufficient accuracy, in contrast to models that only use the flow rate and the reactor volume to determine the reaction time. However, the precision and trueness depend on the quality of the empirical data acquired previously in laboratory experiments for the selected temperature and raw material quality.","lang":"eng"}],"keyword":["Continuous mashing","Residence time distribution","Beer","Enzyme bioreactor","Maltose rest"],"status":"public","department":[{"_id":"DEP4023"},{"_id":"DEP1308"},{"_id":"DEP4018"}],"year":"2020","user_id":"83780","quality_controlled":"1","doi":"10.1016/j.bej.2020.107765","date_updated":"2024-07-03T07:08:55Z","article_type":"original","type":"journal_article"},{"type":"conference","author":[{"id":"46416","last_name":"Dörksen","full_name":"Dörksen, Helene","first_name":"Helene"},{"id":"1804","last_name":"Lohweg","full_name":"Lohweg, Volker","first_name":"Volker","orcid":"0000-0002-3325-7887"}],"language":[{"iso":"eng"}],"date_created":"2019-11-25T08:35:48Z","citation":{"van":"Dörksen H, Lohweg V. Multivariate Gaussian Feature Selection. . In: European Conference on Data Analysis (ECDA2018). Paderborn, Germany; 2018.","ama":"Dörksen H, Lohweg V. Multivariate Gaussian Feature Selection. . In: <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany; 2018.","ieee":"H. Dörksen and V. Lohweg, “Multivariate Gaussian Feature Selection. ,” in <i>European Conference on Data Analysis (ECDA2018)</i>, Paderborn, 2018.","chicago":"Dörksen, Helene, and Volker Lohweg. “Multivariate Gaussian Feature Selection. .” In <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany, 2018.","bjps":"<b>Dörksen H and Lohweg V</b> (2018) Multivariate Gaussian Feature Selection. . <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Dörksen, Helene</span> ; <span style=\"font-variant:small-caps;\">Lohweg, Volker</span>: Multivariate Gaussian Feature Selection. . In: <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany, 2018","havard":"H. Dörksen, V. Lohweg, Multivariate Gaussian Feature Selection. , in: European Conference on Data Analysis (ECDA2018), Paderborn, Germany, 2018.","ufg":"<b>Dörksen, Helene/Lohweg, Volker (2018)</b>: Multivariate Gaussian Feature Selection. , in: <i>European Conference on Data Analysis (ECDA2018)</i>, Paderborn, Germany.","chicago-de":"Dörksen, Helene und Volker Lohweg. 2018. Multivariate Gaussian Feature Selection. . In: <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany.","short":"H. Dörksen, V. Lohweg, in: European Conference on Data Analysis (ECDA2018), Paderborn, Germany, 2018.","mla":"Dörksen, Helene, and Volker Lohweg. “Multivariate Gaussian Feature Selection. .” <i>European Conference on Data Analysis (ECDA2018)</i>, 2018.","apa":"Dörksen, H., &#38; Lohweg, V. (2018). Multivariate Gaussian Feature Selection. . In <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany."},"place":"Paderborn, Germany","title":"Multivariate Gaussian Feature Selection. ","date_updated":"2023-03-15T13:49:38Z","department":[{"_id":"DEP5023"}],"user_id":"15514","year":2018,"publication":"European Conference on Data Analysis (ECDA2018)","status":"public","keyword":["multivariate feature selection","Gaussian distribution","linear discriminant analysis"],"_id":"2008","conference":{"name":"European Conference on Data Analysis","start_date":"2018-07-04","end_date":"2018-07-06","location":"Paderborn"},"abstract":[{"lang":"eng","text":"We concentrate our research activities on the multivariate feature selection, which is one important part of many machine learning tasks. In partucular, Linear Discriminant Analysis [1] belongs to the state-of-the-art methods for the multivariate analysis. From the theoretical point of view, it is the well-known fact that LDA is best suitable in the case the features are Gaussian distributed.\r\nIn the theoretical part of the presented paper, we analyse the properties of the multivariate discriminant analysis with respect to the feature selection. In this context, we consider a binary supervised learning task and assume that the features are Gaussian distributed. The discriminant analysis solves the mentioned supervised learning task by maximising of the discriminant value, calculated for the linear combination of the features.\r\nThe initial LDA solution a 2 Rd is considered for all given features from the feature space X \u001a Rd. The corresponding discriminant is calculated by the formula:\r\nd(a; x1, . . . , xd) := (μ+ − μ−)2\r\n\u001b2+\r\n+ \u001b2−\r\n,\r\nwhere μ+/− are projected class means and \u001b2 +/− are projected class variances (with respect to a). We proof several propositions with the aim to find subsets of the features having higher discriminant value as original d(a; x1, . . . , xd). For the suitability in the real world settings, here we are interested in fast searching for such subsets.\r\nThe performance of the mentioned propositions is examined experimentally on datasets from UCI repository [2]. Several application scenarien will be discussed and tested on the datasets. In addition, tests show that the performance can be achieved also in the case the features are not Gaussian distributed."}]},{"series_title":"Lecture Notes in Computer Science","publication":"33rd Annual German Conference on Artificial Intelligence (KI 2010)","_id":"2088","publication_identifier":{"eisbn":["978-3-642-16111-7"],"isbn":["978-3-642-16110-0"]},"intvolume":"      6359","author":[{"full_name":"Niggemann, Oliver","id":"10876","last_name":"Niggemann","first_name":"Oliver"},{"first_name":"Volker","last_name":"Lohweg","id":"1804","full_name":"Lohweg, Volker","orcid":"0000-0002-3325-7887"},{"full_name":"Tack, Tim","last_name":"Tack","first_name":"Tim"}],"publication_status":"published","volume":6359,"date_created":"2019-12-02T08:21:26Z","language":[{"iso":"eng"}],"citation":{"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.","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).","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>","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.","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","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.","short":"O. Niggemann, V. Lohweg, T. Tack, in: 33rd Annual German Conference on Artificial Intelligence (KI 2010), Springer, Berlin, 2010, pp. 184–194.","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>.","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>","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.","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> ."},"place":"Berlin","title":"A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs","main_file_link":[{"url":"https://link.springer.com/chapter/10.1007/978-3-642-16111-7_21"}],"department":[{"_id":"DEP5023"}],"year":2010,"user_id":"45673","status":"public","keyword":["Markov Chain","Cluster Algorithm","Edge Weight","Spectral Cluster","Stable Distribution"],"page":"184-194","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."}],"type":"conference","publisher":"Springer","doi":"https://doi.org/10.1007/978-3-642-16111-7_21","date_updated":"2023-03-15T13:49:38Z"}]