Thematische Bibliographien / Dbsan

Auswahl der wissenschaftlichen Literatur zum Thema „Dbsan“

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Veröffentlicht am 25. Juli 2024

Zuletzt aktualisiert am 26. Juli 2024

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Zeitschriftenartikel zum Thema "Dbsan"

Al-Ameri, Mohammed Abdulbasit Ali, Basim Mahmood, Bünyamin Ciylan und Alaa Amged. „Unsupervised Forgery Detection of Documents: A Network-Inspired Approach“. Electronics 12, Nr. 7 (03.04.2023): 1682. http://dx.doi.org/10.3390/electronics12071682.

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The area of forgery detection of documents is considered an active field of research in digital forensics. One of the most common issues that investigators struggle with is circled around the selection of the approach in terms of accuracy, complexity, cost, and ease of use. The literature includes many approaches that are based on either image processing techniques or spectrums analysis. However, most of the available approaches have issues related to complexity and accuracy. This article suggests an unsupervised forgery detection framework that utilizes the correlations among the spectrums of documents’ matters in generating a weighted network for the tested documents. The network, then, is clustered using several unsupervised clustering algorithms. The detection rate is measured according to the number of network clusters. Based on the obtained results, our approach provides high accuracy using the Louvain clustering algorithms, while the use of the updated version of the DBSAN was more successful when testing many documents at the same time. Additionally, the suggested framework is considered simple to implement and does not require professional knowledge to use.

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Rodríguez L., Ingrid, César Honorio J., Julia Ramírez S., Zara León G. und Willman Alarcón G. „Efecto de un anticoccidial natural a base de saponinas de Yucca schidigera y Trigonella foenum-graecum sobre el control de coccidiosis en pollos de carne“. Revista de Investigaciones Veterinarias del Perú 30, Nr. 3 (10.10.2019): 1196–206. http://dx.doi.org/10.15381/rivep.v30i3.16597.

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El objetivo del estudio fue evaluar el efecto de un anticoccidial natural a base de saponinas procedentes de Yucca schidigera y Trigonella foenum-graecumen en el control de coccidiosis en pollos de carne. Se utilizaron 75 pollos machos de la línea Cobb 500 de un día de edad, distribuidos al azar en tres tratamientos: DBSA (dieta base sin adición de anticoccidial), DBAN (dieta base con adición de anticoccidial natural), DBQI (dieta base con adición de anticoccidial químico - ionóforo) con cinco repeticiones y cinco aves por unidad experimental. Los pollos fueron inoculados en día 14 con 15 veces la dosis recomendada de la vacuna viva (Coccivac-D), que contiene Eimeria acervulina, E. maxima, E. mivati, E. tenella, E. necatrix, E. brunetti, E. hagani y E. praecox para inducir la enfermedad. Se evaluaron las variables recuento de ooquistes por gramo de heces (ROpgh), lesiones intestinales y diversos parámetros productivos. Los valores de ROpgh fueron similares para DBAN y DBQI llegando a su pico a la séptima semana (328.8 y 455.8 Opgh, respectivamente), para luego disminuir. Las lesiones intestinales los días 10, 20 y 28 pos-infección y los parámetros productivos al final del estudio fueron similares para ambos tratamientos, pero significativamente mejores que el control DBSA. Se concluye que las saponinas procedentes de Y. schidigera y T. foenum-graecum pueden remplazar eficientemente a los anticoccidiales químicos en la alimentación de las aves.

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Lv, Yikun, He Jiang und Pinchen Pan. „NI-DBSCAN: DBSCAN under Non-IID“. Journal of Physics: Conference Series 1533 (April 2020): 022110. http://dx.doi.org/10.1088/1742-6596/1533/2/022110.

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Lulli, Alessandro, Matteo Dell'Amico, Pietro Michiardi und Laura Ricci. „NG-DBSCAN“. Proceedings of the VLDB Endowment 10, Nr. 3 (November 2016): 157–68. http://dx.doi.org/10.14778/3021924.3021932.

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Giri, Kinsuk, Tuhin Kr Biswas und Pritisha Sarkar. „ECR-DBSCAN: An improved DBSCAN based on computational geometry“. Machine Learning with Applications 6 (Dezember 2021): 100148. http://dx.doi.org/10.1016/j.mlwa.2021.100148.

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Feng, Ling, Kejian Liu, Fuxi Tang und Qingrui Meng. „GO-DBSCAN: Improvements of DBSCAN Algorithm Based on Grid“. International Journal of Computer Theory and Engineering 9, Nr. 3 (2017): 151–55. http://dx.doi.org/10.7763/ijcte.2017.v9.1129.

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Schubert, Erich, Jörg Sander, Martin Ester, Hans Peter Kriegel und Xiaowei Xu. „DBSCAN Revisited, Revisited“. ACM Transactions on Database Systems 42, Nr. 3 (24.08.2017): 1–21. http://dx.doi.org/10.1145/3068335.

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Chen, Guangsheng, Yiqun Cheng und Weipeng Jing. „DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark“. International Journal of High Performance Computing and Networking 13, Nr. 4 (2019): 417. http://dx.doi.org/10.1504/ijhpcn.2019.099265.

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Jing, Weipeng, Guangsheng Chen und Yiqun Cheng. „DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark“. International Journal of High Performance Computing and Networking 13, Nr. 4 (2019): 417. http://dx.doi.org/10.1504/ijhpcn.2019.10020624.

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Cheng, Dongdong, Cheng Zhang, Ya Li, Shuyin Xia, Guoyin Wang, Jinlong Huang, Sulan Zhang und Jiang Xie. „GB-DBSCAN: A fast granular-ball based DBSCAN clustering algorithm“. Information Sciences 674 (Juli 2024): 120731. http://dx.doi.org/10.1016/j.ins.2024.120731.

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Dissertationen zum Thema "Dbsan"

Neto, AntÃnio Cavalcante AraÃjo. „G2P-DBSCAN: Data Partitioning Strategy and Distributed Processing of DBSCAN with MapReduce“. Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=15592.

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CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior
Clustering is a data mining technique that brings together elements of a data set such so that the elements of a same group are more similar to each other than to those from other groups. This thesis studied the problem of processing the clustering based on density DBSCAN algorithm distributedly through the MapReduce paradigm. In the distributed processing it is important that the partitions are processed have approximately the same size, provided that the total of the processing time is limited by the time the node with a larger amount of data leads to complete the computation of data assigned to it. For this reason we also propose a data set partitioning strategy called G2P, which aims to distribute the data set in a balanced manner between partitions and takes into account the characteristics of DBSCAN algorithm. More Specifically, the G2P strategy uses grid and graph structures to assist in the division of space low density regions. Distributed DBSCAN the algorithm is done processing MapReduce two stages and an intermediate phase that identifies groupings that can were divided into more than one partition, called candidates from merging. The first MapReduce phase applies the algorithm DSBCAN the partitions individually. The second and checks correcting, if necessary, merge candidate clusters. Experiments using data sets demonstrate that true G2P-DBSCAN strategy overcomes the baseline adopted in all the scenarios, both at runtime and quality of obtained partitions.
ClusterizaÃao Ã uma tÃcnica de mineraÃÃo de dados que agrupa elementos de um conjunto de dados de forma que os elementos que pertencem ao mesmo grupo sÃo mais semelhantes entre si que entre elementos de outros grupos. Nesta dissertaÃÃo nÃs estudamos o problema de processar o algoritmo de clusterizaÃÃo baseado em densidade DBSCAN de maneira distribuÃda atravÃs do paradigma MapReduce. Em processamentos distribuÃdos Ã importante que as partiÃÃes de dados a serem processadas tenham tamanhos proximadamente iguais, uma vez que o tempo total de processamento Ã delimitado pelo tempo que o nÃ com uma maior quantidade de dados leva para finalizar a computaÃÃo dos dados a ele atribuÃdos. Por essa razÃo nÃs tambÃm propomos uma estratÃgia de particionamento de dados, chamada G2P, que busca distribuir o conjunto de dados de forma balanceada entre as partiÃÃes e que leva em consideraÃÃo as caracterÃsticas do algoritmo DBSCAN. Mais especificamente, a estratÃgia G2P usa estruturas de grade e grafo para auxiliar na divisÃo do espaÃo em regiÃes de baixa densidade. JÃ o processamento distribuÃdo do algoritmo DBSCAN se dÃ por meio de duas fases de processamento MapReduce e uma fase intermediÃria que identifica clusters que podem ter sido divididos em mais de uma partiÃÃo, chamados de candidatos Ã junÃÃo. A primeira fase de MapReduce aplica o algoritmo DSBCAN nas partiÃÃes de dados individualmente, e a segunda verifica e corrige, caso necessÃrio, os clusters candidatos Ã junÃÃo. Experimentos utilizando dados reais mostram que a estratÃgia G2P-DBSCAN se comporta melhor que a soluÃÃo utilizada para comparaÃÃo em todos os cenÃrios considerados, tanto em tempo de execuÃÃo quanto em qualidade das partiÃÃes obtidas.

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Araújo, Neto Antônio Cavalcante. „G2P-DBSCAN: Estratégia de Particionamento de Dados e de Processamento Distribuído fazer DBSCAN com MapReduce“. reponame:Repositório Institucional da UFC, 2016. http://www.repositorio.ufc.br/handle/riufc/16372.

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ARAÚJO NETO, Antônio Cavalcante. G2P-DBSCAN: Estratégia de Particionamento de Dados e de Processamento Distribuído fazer DBSCAN com MapReduce. 2016. 63 f. Dissertação (mestrado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2016.
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Clustering is a data mining technique that brings together elements of a data set such so that the elements of a same group are more similar to each other than to those from other groups. This thesis studied the problem of processing the clustering based on density DBSCAN algorithm distributedly through the MapReduce paradigm. In the distributed processing it is important that the partitions are processed have approximately the same size, provided that the total of the processing time is limited by the time the node with a larger amount of data leads to complete the computation of data assigned to it. For this reason we also propose a data set partitioning strategy called G2P, which aims to distribute the data set in a balanced manner between partitions and takes into account the characteristics of DBSCAN algorithm. More Specifically, the G2P strategy uses grid and graph structures to assist in the division of space low density regions. Distributed DBSCAN the algorithm is done processing MapReduce two stages and an intermediate phase that identifies groupings that can were divided into more than one partition, called candidates from merging. The first MapReduce phase applies the algorithm DSBCAN the partitions individually. The second and checks correcting, if necessary, merge candidate clusters. Experiments using data sets demonstrate that true G2P-DBSCAN strategy overcomes the baseline adopted in all the scenarios, both at runtime and quality of obtained partitions.
Clusterizaçao é uma técnica de mineração de dados que agrupa elementos de um conjunto de dados de forma que os elementos que pertencem ao mesmo grupo são mais semelhantes entre si que entre elementos de outros grupos. Nesta dissertação nós estudamos o problema de processar o algoritmo de clusterização baseado em densidade DBSCAN de maneira distribuída através do paradigma MapReduce. Em processamentos distribuídos é importante que as partições de dados a serem processadas tenham tamanhos proximadamente iguais, uma vez que o tempo total de processamento é delimitado pelo tempo que o nó com uma maior quantidade de dados leva para finalizar a computação dos dados a ele atribuídos. Por essa razão nós também propomos uma estratégia de particionamento de dados, chamada G2P, que busca distribuir o conjunto de dados de forma balanceada entre as partições e que leva em consideração as características do algoritmo DBSCAN. Mais especificamente, a estratégia G2P usa estruturas de grade e grafo para auxiliar na divisão do espaço em regiões de baixa densidade. Já o processamento distribuído do algoritmo DBSCAN se dá por meio de duas fases de processamento MapReduce e uma fase intermediária que identifica clusters que podem ter sido divididos em mais de uma partição, chamados de candidatos à junção. A primeira fase de MapReduce aplica o algoritmo DSBCAN nas partições de dados individualmente, e a segunda verifica e corrige, caso necessário, os clusters candidatos à junção. Experimentos utilizando dados reais mostram que a estratégia G2P-DBSCAN se comporta melhor que a solução utilizada para comparação em todos os cenários considerados, tanto em tempo de execução quanto em qualidade das partições obtidas.

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Mahmod, Shad. „Deinterleaving pulse trains with DBSCAN and FART“. Thesis, Uppsala universitet, Avdelningen för systemteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-379718.

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Studying radar pulses and looking for certain patterns is critical in order to assess the threat level of the environment around an antenna. In order to study the electromagnetic pulses emitted from a certain radar, one must first register and identify these pulses. Usually there are several active transmitters in anenvironment and an antenna will register pulses from various sources. In order to study the different pulse trains, the registered pulses first have to be sorted sothat all pulses that are transmitted from one source are grouped together. This project aims to solve this problem, using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and compare the results with those obtained by Fuzzy Adaptive Resonance Theory (FART). We aim to further dig into these methods and map out how factors such as feature selection and training time affects the results. A solution based on the DBSCAN method is proposed which allows online clustering of new points introduced to the system. The methods are implemented and tested on simulated data. The data consists of pulse trains from simulated transmitters with unique behaviors. The deployed methods are then tested varying the parameters of the models as well as the number of pulse trains they are asked to deinterleave. The results when applying the models are then evaluated using the adjusted Rand index (ARI). The results indicate that in most cases using all possible data (in this case the angle of arrival, radio frequency, pulse width and amplitudes of the pulses) generate the best results. Rescaling the data further improves the result and tuning the parameters shows that the models work well when increasing the number of emitters. The results also indicate that the DBSCAN method can be used to get accurate estimates of the number of emitters transmitting. The online DBSCAN generates a higher ARI than FART on the simulated data set but has a higher worst case computational cost.

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Кулік, Євгенія Сергіївна, Евгения Сергеевна Кулик, Євгенія Сергіївна Кулік, Захар Вікторович Козлов, Захар Викторович Козлов und Zakhar Viktorovych Kozlov. „Використання SR-дерев у щільнісному методі кластеризації числових просторів DBSCAN“. Thesis, Cумський державний університет, 2016. http://essuir.sumdu.edu.ua/handle/123456789/46528.

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Алгоритм щільнісного кластерного аналізу DBSCAN при обробці просторових даних дуже часто потребує знаходження ε-околу точки у n-вимірному просторі. Цей крок алгоритму виконується для кожної точки щонайменше один раз [1], отже покращення ефективності знаходження ε-околу матиме значний вплив на результати роботи алгоритму загалом.

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Kästel, Arne Morten, und Christian Vestergaard. „Comparing performance of K-Means and DBSCAN on customer support queries“. Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260252.

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In customer support, there are often a lot of repeat questions, and questions that does not need novel answers. In a quest to increase the productivity in the question answering task within any business, there is an apparent room for automatic answering to take on some of the workload of customer support functions. We look at clustering corpora of older queries and texts as a method for identifying groups of semantically similar questions and texts that would allow a system to identify new queries that fit a specific cluster to receive a connected, automatic response. The approach compares the performance of K-means and density-based clustering algorithms on three different corpora using document embeddings encoded with BERT. We also discuss the digital transformation process, why companies are unsuccessful in their implementation as well as the possible room for a new more iterative model.
I kundtjänst förekommer det ofta upprepningar av frågor samt sådana frågor som inte kräver unika svar. I syfte att öka produktiviteten i kundtjänst funktionens arbete att besvara dessa frågor undersöks metoder för att automatisera en del av arbetet. Vi undersöker olika metoder för klusteranalys, applicerat på existerande korpusar innehållande texter så väl som frågor. Klusteranalysen genomförs i syfte att identifiera dokument som är semantiskt lika, vilket i ett automatiskt system för frågebevarelse skulle kunna användas för att besvara en ny fråga med ett existerande svar. En jämförelse mellan hur K-means och densitetsbaserad metod presterar på tre olika korpusar vars dokumentrepresentationer genererats med BERT genomförs. Vidare diskuteras den digitala transformationsprocessen, varför företag misslyckas avseende implementation samt även möjligheterna för en ny mer iterativ modell.

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Legoabe, Reginald Sethole. „An Impact Assessment of the DBSA/ SALGA ICT Internship Programme: A Case Study“. Thesis, North-West University (South Africa), 2010. http://hdl.handle.net/10919/71530.

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The aim of this descriptive and evaluative research study is to assess the impact achieved by the DBSA/ SALGA ICT Internship Programme, a national local government internship programme that was undertaken by South African Local Government Association (SALGA) and the Development Bank of Southern Africa (DBSA) Development Fund in partnership with the South African Communication Forum (SACF), Department of Provincial and Local Government (DPLG) and SIEMENS Ltd Training Institute.A supply-side internship programme in nature, its strategic objectives were to train and equip young South Africans with ICT skills, give youth learners workplace experience in the ICT functional area within their respective municipalities, create employment opportunities for youth and economic development for local municipalities in alleviation of scarce-critical ICT skills to capacitate the local government sector.Forty (x40) learners from Further Education and Training (FET) Colleges were recruited from various rural municipalities to undertake ICT training with SIEMENS Training Institute and given workplace experiential learning with fifteen x15 host municipalities under the banner of the South African Local Government Association. This descriptive and evaluative study is undertaken in a case study format with particular interest in the retention levels of graduate learners endowed with scarce skills in the context of the skills challenges facing the local government sector. The study also focuses on unique challenges and interventional measures that could be undertaken by designers of public education and training programmes to ensure efficiency of internship programmes and optimal benefit of publicly-funded internship programmes to youth learners. This research study not only has internal validity in terms of the operational delivery of internship programmes but also external contextual importance for publicly-funded learning and placement programmes within the larger human resources development (HRD) domain and local government sector. In the process of conducting the study, stratified random sampling is utilised due to the multi-stakeholder nature of the programme. A stratified survey sample comprising fifty percent (50%) of the total survey population of forty (40) former ICT learners who participated in the internship programme is selected whilst a sample of sixty percent (60%) of the fifteen (15) host municipalities who participated in the programme is also selected using stratified random sampling. The findings of the study indicate that participation in the DBSA/ SALGA ICT Internship Programme has positively promoted the employability of former ICT learners. All ICT learner respondents confirmed current employment within the ICT functional area. Research findings indicate that the local government sector has derived short term retention and benefit from the programme but has not been able to retain the skills of the majority of former ICT learners in the long term. Although most of the former ICT learners have since migrated out of the local government sector, most former learners are still employed in the ICT field within the public sector and to some extent in the private sector of the South African economy. The study found out that most learners were able to assimilate and find employment within their host municipalities or were able to find ICT-related employment soon after graduation. The research findings of this impact assessment study indicate that the DBSA/ SALGA ICT Internship Programme has positively transformed young inexperienced graduates into responsible young adults through the development of key life skills and work experiences to enable them to successfully navigate the path between the classroom and the challenging world of work.
Mini-dissertation submitted in partial fullfilment of the requirements for the North-West University Yunibesiti Ya Bokone-Bophirima Noordwest-Universiteit Masters Degree in Business Administration (MBA) Human Resource Management (HRM) North-West University (NWU) Graduate School of Business & Government

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Мельникова, П. А. „Поиск аномалий во временных рядах на основе оценивания их параметров“. Thesis, ХНУРЕ, 2021. https://openarchive.nure.ua/handle/document/16436.

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Anomaly detection in every domain is an important topic of discussion and the knowledge about the effective ways to perform anomaly detection is an important skill to have for any Data Scientist. A problem of anomaly detection in time series is considered. A method is proposed, which allows to detect anomalies in time series efficiently by their parameters.

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Крамар, Іван Ігорович. „Кластеризація даних, що збираються з відібраних джерел науково-технічної інформації“. Bachelor's thesis, КПІ ім. Ігоря Сікорського, 2020. https://ela.kpi.ua/handle/123456789/36639.

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Метою роботи є застосування кластеризації науково-технічних даних не тільки для наглядного представлення об’єктів, але і для розпізнавання нових. Метою кластеризації документів є автоматичне виявлення груп семантично схожих документів серед заданої фіксованої множини. Групи формуються тільки на основі попарної схожості описів документів, і ніякі характеристики цих груп не задаються заздалегідь. Для видалення неінформативних слів розглянуто методи: видалення стоп-слів, стеммінг, N-діаграми, приведення регістра. Для виділення ключових слів та класифікації результатів використано наступні методи: словниковий, статистичний та побудований на основі Y-інтерпретації закону Бредфорда, TF-IDF міра, F-міра та метод лакричних шаблонів. Для реалізації системи кластерного аналізу науково-технічних даних обрано високорівневу мову програмування Python, реалізація інтерпретатора 2.7. Даний програмний код читається легше, його багаторазове використання і обслуговування виконується набагато простіше, ніж використання програмного коду на інших мовах.
The aim of the work is to use the clustering of scientific and technical data not only for the visual representation of objects, but also for the recognition of new ones. The purpose of document clustering is to automatically detect groups of semantically similar documents among a given fixed set. Groups are formed only on the basis of pairwise similarity of document descriptions, and no characteristics of these groups are set in advance. Methods for deleting uninformative words are considered: deletion of stop words, stemming, N-diagrams, case reduction. The following methods were used to highlight keywords and classify the results: dictionary, statistical and based on the Y-interpretation of Bradford's law, TF-IDF measure, F-measure and the method of licorice patterns. Python programming language was chosen to implement the system of cluster analysis of scientific and technical data, a high-level, the implementation of the interpreter 2.7. This program code is easier to read, its reuse and maintenance is much easier than using program code in other languages.
Целью работы является применение кластеризации научно-технических данных не только для наглядного представления объектов, но и для распознавания новых. Целью кластеризации документов является автоматическое выявление групп семантически похожих документов среди заданной фиксированной множества. Группы формируются только на основе попарно сходства описаний документов, и никакие характеристики этих групп не задаются заранее. Для удаления неинформативных слов рассмотрены методы: удаление стоп-слов, стемминг, N-диаграммы, приведение регистра. Для выделения ключевых слов и классификации результатов использованы следующие методы: словарный, статистический и построен на основе Y-интерпретации закона Брэдфорда, TF-IDF мера, F-мера и способ лакричным шаблонов. Для реализации системы кластерного анализа научно-технических данных избран высокоуровневый язык программирования Python, реализация интерпретатора 2.7. Данный программный код читается легче, его многократное использование и обслуживание выполняется гораздо проще, чем использование программного кода на других языках.

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Kannamareddy, Aruna Sai. „Density and partition based clustering on massive threshold bounded data sets“. Kansas State University, 2017. http://hdl.handle.net/2097/35467.

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Master of Science
Department of Computing and Information Sciences
William H. Hsu
The project explores the possibility of increasing efficiency in the clusters formed out of massive data sets which are formed using threshold blocking algorithm. Clusters thus formed are denser and qualitative. Clusters that are formed out of individual clustering algorithms alone, do not necessarily eliminate outliers and the clusters generated can be complex, or improperly distributed over the data set. The threshold blocking algorithm, a current research paper from Michael Higgins of Statistics Department on other hand, in comparison with existing algorithms performs better in forming the dense and distinctive units with predefined threshold. Developing a hybridized algorithm by implementing the existing clustering algorithms to re-cluster these units thus formed is part of this project. Clustering on the seeds thus formed from threshold blocking Algorithm, eases the task of clustering to the existing algorithm by eliminating the overhead of worrying about the outliers. Also, the clusters thus generated are more representative of the whole. Also, since the threshold blocking algorithm is proven to be fast and efficient, we now can predict a lot more decisions from large data sets in less time. Predicting the similar songs from Million Song Data Set using such a hybridized algorithm is considered as the data set for the evaluation of this goal.

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Huo, Shiyin. „Detecting Self-Correlation of Nonlinear, Lognormal, Time-Series Data via DBSCAN Clustering Method, Using Stock Price Data as Example“. The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1321989426.

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Bücher zum Thema "Dbsan"

Gibson, Bill. The DBSA macromodel. Halfway House, South Africa: Development Bank of Southern Africa, 1997.

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Grange, A. B. La. DBSA in the southern African development framework. Sandton, Republic of South Africa: Development Bank of Southern Africa, 1985.

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Center, Buddhist Digital Resource. Grub-dban sakya-sri-dznya-na'i gsun 'bum. Kathmandu: Khenpo shedup tenzin and lama thinley namgyal, 1998.

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Center, Buddhist Digital Resource. Lamas of zans-dkar: A collection of manuscript material about the lives of kun-dga'-chos-legs, bla-ma karma, and grub-dban nag-dban-tshe-rin. Gemur, distt. lahul: Tobden tsering, 1985.

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Center, Buddhist Digital Resource. Collected works sun 'bum of tenth pan-chen chos-kyi-dban-phyug. Delhi: Tashi lhunpo monastery, 1998.

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Van der Kooy, R. J. W., Hrsg. An Introduction to economic development in Southern Africa and the role of DBSA. Sandton, Republic of South Africa: Development Bank of Southern Africa, 1985.

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Kirsten, Marié. DBSA infrastructure barometer, 2008: Economic and social infrastructure in South Africa : scenarios for the future. Herausgegeben von Development Bank of Southern Africa. [Halfway House, Midrand, South Africa]: Development Bank of Southern Africa, 2008.

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Center, Buddhist Digital Resource. Initiation texts (dban dpe) of the bhutanese tradition of the 'brug-pa dkar-brgyud-pa. Rewalsar, distt. mandi, h.p: Zigar drukpa kargyud institute, 1985.

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Center, Buddhist Digital Resource. Mkhas btsun bzan po'i bdag nid mtshuns med chos kyi rje nag dban rgyal mtshan gyi rba rlabs mnon par g'yo ba: The biography of se'u-la byams-mgon nag-dban-rgyal-mtshan (1647-1732). Thimphu, bhutan: National library of bhutan, 1985.

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Center, Buddhist Digital Resource. The collected works (gsun 'bum) of nin-rdzon khri-pa dkon-mchog-don-grub-chos-dban. Bir, h.p: Bir tibetan society, 1985.

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Buchteile zum Thema "Dbsan"

Bhardwaj, Surbhi, und Subrat Kumar Dash. „VDMR-DBSCAN: Varied Density MapReduce DBSCAN“. In Big Data Analytics, 134–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27057-9_10.

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Dierkes, Joel, Daniel Stelter und Christian Braune. „$$\lambda $$-DBSCAN: Augmenting DBSCAN with Prior Knowledge“. In Lecture Notes in Computer Science, 107–18. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58553-1_9.

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Ashour, Wesam, und Saad Sunoallah. „Multi Density DBSCAN“. In Lecture Notes in Computer Science, 446–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23878-9_53.

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Li, Tianrun, Thomas Heinis und Wayne Luk. „Hashing-Based Approximate DBSCAN“. In Advances in Databases and Information Systems, 31–45. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44039-2_3.

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Kryszkiewicz, Marzena, und Piotr Lasek. „TI-DBSCAN: Clustering with DBSCAN by Means of the Triangle Inequality“. In Rough Sets and Current Trends in Computing, 60–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13529-3_8.

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Nguyen, Thi Quynh, Romain Laborde, Abdelmalek Benzekri, Arnaud Oglaza und Mehdi Mounsif. „AutoRoC-DBSCAN: Automatic Tuning of DBSCAN to Detect Malicious DNS Tunnels“. In Communications in Computer and Information Science, 126–44. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23098-1_8.

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Scicluna, Neil, und Christos-Savvas Bouganis. „FPGA-Based Parallel DBSCAN Architecture“. In Lecture Notes in Computer Science, 1–12. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05960-0_1.

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Bordogna, Gloria, und Dino Ienco. „Fuzzy Core DBScan Clustering Algorithm“. In Information Processing and Management of Uncertainty in Knowledge-Based Systems, 100–109. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08852-5_11.

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Arlia, Domenica, und Massimo Coppola. „Experiments in Parallel Clustering with DBSCAN“. In Euro-Par 2001 Parallel Processing, 326–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44681-8_46.

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Braune, Christian, Stephan Besecke und Rudolf Kruse. „Density Based Clustering: Alternatives to DBSCAN“. In Partitional Clustering Algorithms, 193–213. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09259-1_6.

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Konferenzberichte zum Thema "Dbsan"

Ding, Hu, Fan Yang und Mingyue Wang. „On Metric DBSCAN with Low Doubling Dimension“. In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/426.

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The density based clustering method Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a popular method for outlier recognition and has received tremendous attention from many different areas. A major issue of the original DBSCAN is that the time complexity could be as large as quadratic. Most of existing DBSCAN algorithms focus on developing efficient index structures to speed up the procedure in low-dimensional Euclidean space. However, the research of DBSCAN in high-dimensional Euclidean space or general metric spaces is still quite limited, to the best of our knowledge. In this paper, we consider the metric DBSCAN problem under the assumption that the inliers (excluding the outliers) have a low doubling dimension. We apply a novel randomized k-center clustering idea to reduce the complexity of range query, which is the most time consuming step in the whole DBSCAN procedure. Our proposed algorithms do not need to build any complicated data structures and are easy to implement in practice. The experimental results show that our algorithms can significantly outperform the existing DBSCAN algorithms in terms of running time.

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Pathak, Sashakt, Arushi Agarwal, Ankita Ankita und Mahendra Kumar Gurve. „Restricted Randomness DBSCAN : A faster DBSCAN Algorithm“. In IC3 '21: 2021 Thirteenth International Conference on Contemporary Computing. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3474124.3474204.

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Ventorim, Igor, Diego Luchi und Flávio Varejão. „Um método de amostragem tendenciosa para aplicação do DBSCAN“. In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/eniac.2020.12129.

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O algoritmo DBSCAN é um método de agrupamento baseado em densidade. Permite identificar grupos de diferentes formatos topológicos e também descartar ruídos isolados. O DBSCAN normalmente apresenta bons resultados, no entanto, ele realiza diversos cálculos de distâncias no processo de agrupamento, possuindo baixa eficiência em grandes conjuntos de dados. Neste trabalho é apresentado um novo método amostral que possibilita aplicar o DBSCAN em um conjunto reduzido de exemplos, aproximando-se do resultado original do algoritmo aplicado sobre todo o conjunto de dados. Portanto, o método torna possível a execução de grandes conjuntos de dados com resultados semelhantes ao do DBSCAN na base de dados completa, porém, com maior eficiência.

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Song, Hwanjun, und Jae-Gil Lee. „RP-DBSCAN“. In SIGMOD/PODS '18: International Conference on Management of Data. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3183713.3196887.

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Gan, Junhao, und Yufei Tao. „DBSCAN Revisited“. In SIGMOD/PODS'15: International Conference on Management of Data. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2723372.2737792.

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Kisilevich, Slava, Florian Mansmann und Daniel Keim. „P-DBSCAN“. In the 1st International Conference and Exhibition. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1823854.1823897.

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Nagarajan, Vani, und Milind Kulkarni. „RT-DBSCAN: Accelerating DBSCAN using Ray Tracing Hardware“. In 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2023. http://dx.doi.org/10.1109/ipdps54959.2023.00100.

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Payghan, Vaibhav Santosh, Miit Prajapati und Abhisha Chauhan. „A Novel Method to Select Hyperparameters of the DBSCAN Algorithm for RADAR Applications“. In Symposium on International Automotive Technology. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-26-0030.

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<div class="section abstract"><div class="htmlview paragraph">As the automotive industry is coming up with various ADAS solutions, RADAR is playing an important role. There are many parameters concerning RADAR detections to acknowledge. Unsupervised Clustering methods are used for RADAR applications. DBSCAN clustering method which is widely used for RADAR applications. The existing clustering DBSCAN is not aligned very well with its hyperparameters such as epsilon (the radius within which each data point checks the density) and minimum points (minimum data points required within a circle to check for core point) for which a calibration is needed. In this paper, different methods to choose the hyperparameters of DBSCAN are compared and verified with different clustering evaluation criteria. <b>A novel method to select hyperparameters of the DBSCAN algorithm</b> is presented with the paper. For testing the given algorithm, ground truth data is collected, and the results are verified with MATLAB-Simulink.</div></div>

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Bessrour, Malek, Zied Elouedi und Eric Lefevre. „E-DBSCAN: An evidential version of the DBSCAN method“. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2020. http://dx.doi.org/10.1109/ssci47803.2020.9308578.

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Tekbir, Mennan, und Songul Albayrak. „Recursive-Partitioned DBSCAN“. In 2010 IEEE 18th Signal Processing and Communications Applications Conference (SIU). IEEE, 2010. http://dx.doi.org/10.1109/siu.2010.5651189.

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