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Relevant bibliographies by topics / Dbsan

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Dissertations / Theses

Academic literature on the topic 'Dbsan'

Author: Grafiati

Published: 25 July 2024

Last updated: 30 July 2024

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Dissertations / Theses on the topic "Dbsan"

1

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<br>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 am

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2

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.<br>Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-03-22T19:21:02Z No. of bitstreams: 1 2016_dis_acaraujoneto.pdf: 5671232 bytes, checksum: ce91a85d087f63206ad938133c163560 (MD5)<br>Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-04-25T12:33:12Z (GMT) No. of bitstreams: 1 2016_dis_acaraujoneto.pdf: 567

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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, us

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4

Кулік, Євгенія Сергіївна, Евгения Сергеевна Кулик, Євгенія Сергіївна Кулік, Захар Вікторович Козлов, Захар Викторович Козлов та 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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5

Kästel, Arne Morten, and 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

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6

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 wit

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7

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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8

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 мето

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9

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<br>Department of Computing and Information Sciences<br>William H. Hsu<br>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 Depar

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10

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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