Relevant bibliographies by topics / Mining methods

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  1. Dissertations / Theses

Academic literature on the topic 'Mining methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Dissertations / Theses on the topic "Mining methods"

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Mwitondi, K. S. "Robust methods in data mining." Thesis, University of Leeds, 2003. http://etheses.whiterose.ac.uk/807/.

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The thesis focuses on two problems in Data Mining, namely clustering, an exploratory technique to group observations in similar groups, and classification, a technique used to assign new observations to one of the known groups. A thorough study of the two problems, which are also known in the Machine Learning literature as unsupervised and supervised classification respectively, is central to decision making in different fields - the thesis seeks to contribute towards that end. In the first part of the thesis we consider whether robust methods can be applied to clustering - in particular, we p
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Wirta, Valtteri. "Mining the transcriptome - methods and applications." Doctoral thesis, Stockholm : School of Biotechnology, Royal Institute of Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4115.

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Siddiqui, Muazzam. "DATA MINING METHODS FOR MALWARE DETECTION." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2783.

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This research investigates the use of data mining methods for malware (malicious programs) detection and proposed a framework as an alternative to the traditional signature detection methods. The traditional approaches using signatures to detect malicious programs fails for the new and unknown malwares case, where signatures are not available. We present a data mining framework to detect malicious programs. We collected, analyzed and processed several thousand malicious and clean programs to find out the best features and build models that can classify a given program into a malware or a clean
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Espinoza, Sofia Elizabeth. "Data mining methods applied to healthcare problems." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44903.

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Growing adoption of health information technologies is allowing healthcare providers to capture and store enormous amounts of patient data. In order to effectively use this data to improve healthcare outcomes and processes, clinicians need to identify the relevant measures and apply the correct analysis methods for the type of data at hand. In this dissertation, we present various data mining and statistical methods that could be applied to the type of datasets that are found in healthcare research. We discuss the process of identification of appropriate measures and statistical tools, the ana
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Vu, Lan. "High performance methods for frequent pattern mining." Thesis, University of Colorado at Denver, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3667246.

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<p> Current Big Data era is generating tremendous amount of data in most fields such as business, social media, engineering, and medicine. The demand to process and handle the resulting "big data" has led to the need for fast data mining methods to develop powerful and versatile analysis tools that can turn data into useful knowledge. Frequent pattern mining (FPM) is an important task in data mining with numerous applications such as recommendation systems, consumer market analysis, web mining, network intrusion detection, etc. We develop efficient high performance FPM methods for large-scale
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SOUZA, Ellen Polliana Ramos. "Swarm optimization clustering methods for opinion mining." Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/25227.

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Submitted by Pedro Barros (pedro.silvabarros@ufpe.br) on 2018-07-25T19:46:45Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) TESE Ellen Polliana Ramos Souza.pdf: 1140564 bytes, checksum: 0afe0dc25ea5b10611d057c23af46dec (MD5)<br>Approved for entry into archive by Alice Araujo (alice.caraujo@ufpe.br) on 2018-07-26T21:58:03Z (GMT) No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) TESE Ellen Polliana Ramos Souza.pdf: 1140564 bytes, checksum: 0afe0dc25ea5b10611d057c23af46dec (MD5)<br>Made available in D
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Johnson, Eamon B. "Methods in Text Mining for Diagnostic Radiology." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1459514073.

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Eales, James Matthew. "Text-mining of experimental methods in phylogenetics." Thesis, University of Manchester, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.529251.

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Sundaravej, Dilokpol. "Predictive methods for subsidence due to longwall mining." Ohio : Ohio University, 1986. http://www.ohiolink.edu/etd/view.cgi?ohiou1183379335.

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Bastos, Guilherme Sousa. "Methods for truck dispatching in open-pit mining." Instituto Tecnológico de Aeronáutica, 2010. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=1098.

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Material transportation is one of the most important aspects of open-pit mine operations. The problem usually involves a truck dispatching system in which decisions on truck assignments and destinations are taken in real-time. Due to its significance, several decision systems for this problem have been developed in the last few years, improving productivity and reducing operating costs. As in many other real-world applications, the assessment and correct modeling of uncertainty is a crucial requirement as the unpredictability originated from equipment faults, weather conditions, and human mist
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