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Dissertations / Theses on the topic 'Mining statistics'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

Luo, Man. "Data mining and classical statistics." Virtual Press, 2004. http://liblink.bsu.edu/uhtbin/catkey/1304657.

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This study introduces an overview of data mining. It suggests that methods derived from classical statistics are an integrated part of data mining. However, there are substantial differences between these two areas. Classical statistical models and non-statistical models used in data mining, such as regression trees and artificial neural networks, are presented to emphasize their unique approaches to extract information from data. In summation, this research provides some background to data mining and the role of classical statistics played in it.<br>Department of Mathematical Sciences

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2

Kamimura, Roy T. (Roy Tomoo). "Application of multivariate statistics to fermentation database mining." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/17437.

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3

Espy, John. "Data mining techniques for constructing jury selection models." Thesis, California State University, Long Beach, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1527548.

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<p> Jury selection can determine a case before it even begins. The goal is to predict whether a juror rules for the plaintiff or the defense in the medical malpractice trials that are conducted, and which variables are significant in predicting this. The data for the analysis were obtained from mock trials that simulated actual trials, with possible arguments from the defense and the plaintiff with ample discussion time. These mock trials were supplemented by surveys that attempted to capture the characteristics and attitudes of the mock juror and the case at hand. The data were modeled using

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4

Sharma, Gaurav Kumar. "Bayesian statistics and production reliability assessments for mining operations." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2741.

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This thesis presents a novel application of structural reliability concepts to assess the reliability of mining operations. “Limit-states” are defined to obtain the probability that the total productivity — measured in production time or economic gain — exceeds user-selected thresholds. Focus is on the impact of equipment downtime and other non-operating instances on the productivity and the economic costs of the operation. A comprehensive set of data gathered at a real-world mining facility is utilized to calibrate the probabilistic models. In particular, the utilization of Bayesian inference

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5

Li, Bin. "Statistical learning and predictive modeling in data mining." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155058111.

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6

XU, Chen. "Customer lifetime value : an integrated data mining approach." Digital Commons @ Lingnan University, 2006. https://commons.ln.edu.hk/cds_etd/3.

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Customer Lifetime Value (CLV) ---which is a measure of the profit generating potential, or value, of a customer---is increasingly being considered a touchstone for customer relationship management. As the guide and benchmark for Customer Relationship Management (CRM) applications, CLV analysis has received increasing attention from both the marketing practitioners and researchers from different domains. Furthermore, the central challenge in predicting CLV is the precise calculation of customer’s length of service (LOS). There are several statistical approaches for this problem and several rese

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7

Annabathula, Ramesh. "A Web-based tool for analysis of crime laboratory data." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5048.

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Thesis (M.S.)--West Virginia University, 2007.<br>Title from document title page. Document formatted into pages; contains ix, 110 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 100-102).

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8

Padhye, Manoday D. "Use of data mining for investigation of crime patterns." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4836.

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Thesis (M.S.)--West Virginia University, 2006.<br>Title from document title page. Document formatted into pages; contains viii, 108 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 80-81).

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9

Wang, Dan Tong. "Outlier detection with data stream mining approach in high-dimenional time series data." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691091.

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10

Haider, Syed Imran, and Raja M. Khurram Shahzad. "Detection of Spyware by Mining Executable Files." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3095.

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Malicious programs have been a serious threat for the confidentiality, integrity and availability of a system. Different researches have been done to detect them. Two approaches have been derived for it i.e. Signature Based Detection and Heuristic Based Detection. These approaches performed well against known malicious programs but cannot catch the new malicious programs. Different researchers tried to find new ways of detecting malicious programs. The application of data mining and machine learning is one of them and has shown good results compared to other approaches. A new category of malic

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11

Lee, Kichun. "Functional data mining with multiscale statistical procedures." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34716.

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Hurst exponent and variance are two quantities that often characterize real-life, highfrequency observations. We develop the method for simultaneous estimation of a timechanging Hurst exponent H(t) and constant scale (variance) parameter C in a multifractional Brownian motion model in the presence of white noise based on the asymptotic behavior of the local variation of its sample paths. We also discuss the accuracy of the stable and simultaneous estimator compared with a few selected methods and the stability of computations that use adapted wavelet filters. Multifractals have become popular

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12

Khodabandelou, Ghazaleh. "Mining Intentional Process Models." Phd thesis, Université Panthéon-Sorbonne - Paris I, 2014. http://tel.archives-ouvertes.fr/tel-01010756.

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Jusqu'à présent, les techniques de fouille de processus ont modélisé les processus en termes des séquences de tâches qui se produisent lors de l'exécution d'un processus. Cependant, les recherches en modélisation du processus et de guidance ont montrée que de nombreux problèmes, tels que le manque de flexibilité ou d'adaptation, sont résolus plus efficacement lorsque les intentions sont explicitement spécifiées. Cette thèse présente une nouvelle approche de fouille de processus, appelée Map Miner méthode (MMM). Cette méthode est conçue pour automatiser la construction d'un modèle de processus

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13

He, Ruofei. "Bayesian mixture models for frequent itemset mining." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/bayesian-mixture-models-for-frequent-itemset-mining(6d88d0d1-3066-4545-8565-56d651eeadc4).html.

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In binary-transaction data-mining, traditional frequent itemset mining often produces results which are not straightforward to interpret. To overcome this problem, probability models are often used to produce more compact and conclusive results, albeit with some loss of accuracy. Bayesian statistics have been widely used in the development of probability models in machine learning in recent years and these methods have many advantages, including their abilities to avoid overfitting. In this thesis, we develop two Bayesian mixture models with the Dirichlet distribution prior and the Dirichlet p

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14

Cheng, Wenqian. "Statistical data mining for Sina Weibo, a Chinese micro-blog : sentiment modelling and randomness reduction for topic modelling." Thesis, London School of Economics and Political Science (University of London), 2017. http://etheses.lse.ac.uk/3488/.

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Before the arrival of modern information and communication technology, it was not easy to capture people’s thoughts and sentiments; however, the development of statistical data mining techniques and the prevalence of mass social media provide opportunities to capture those trends. Among all types of social media, micro-blogs make use of the word limit of 140 characters to force users to get straight to thepoint, thus making the posts brief but content-rich resources for investigation. The data mining object of this thesis is Weibo, the most popular Chinese micro-blog. In the first part of the

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15

Parhizi, Shaghayegh. "Measuring nurses' response to configurations of work system parameters a data mining approach." Thesis, University of Missouri - Columbia, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10157761.

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<p> Medical error, patient safety and nurses’ performance are some of the critical concerns within healthcare systems. Several factors contribute to nurses’ performance and patient safety including fatigue, sleepiness and work system parameters.</p><p> Furthermore, because of a shortage of nurses, working nurses are often experiencing high workloads. They often work in 12- hour shifts and/or consecutive night shifts without receiving enough sleep or recovery. Thus, they frequently are fatigued and suffer from sleep deprivation, which again is negatively associated with patient sa

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16

Xiong, Yimin. "Time series clustering using ARMA models /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20XIONG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.<br>Includes bibliographical references (leaves 49-55). Also available in electronic version. Access restricted to campus users.

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17

Knisley, Jeff, L. Lee Glenn, Karl Joplin, and Patricia Carey. "Artificial Neural Networks for Data Mining and Feature Extraction." Digital Commons @ East Tennessee State University, 2007. https://dc.etsu.edu/etsu-works/7520.

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Artificial Neural Networks are models of interacting neurons that can be used as classifiers with large data sets. They can also be used for feature extraction and for reducing the dimensionality of large data sets. Den-Dritic electrotonic models can be used to suggest more robust artificial neural network models that are amenable to data mining and feature extraction.

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18

Janas, Marek J. "Change of support correction in mineral resource estimation." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2001. https://ro.ecu.edu.au/theses/1051.

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The success of any mining operation greatly, if not entirely, depends on the accuracy of prediction of recoverable mining reserves. However, prior to mining, knowledge about the distribution of the Selective Mining Unit (SMU) is limited. The SMU represents the volume on which extraction of ore takes place and on which recoverable mining reserves are based. Realistic recoverable reserve estimates can be obtained from the grade-tonnage curve that corresponds to the unknown distribution of the SMU rather than to the distribution of exploration sample data. In general, if the reserve calculation,

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19

Robles-Stefoni, Lucia. "Critical analysis of multiple-points statistics methods in the stochastic simulation of geology at Fox Kimberlitic Diamond Pipe located on the Ekati Property, North West Territories." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66953.

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Multiple-point simulation (MPS) methods have been developed over the past decade as a mean to generate stochastic simulations while reproducing complex geological patterns, such as high-grade depositional veins, groups of high-grade lentil shaped orebodies, or the spatial geometries and patterns of diamond-bearing kimberlite pipes. This thesis compares two MPS methods by modelling the geology of a diamond pipe located at the Ekati mine, NWT, Canada. The single normal equation simulation algorithm SNESIM, which captures different patterns from a training image (TI), and the fi

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20

Chen, Tian. "Judgment Post-Stratication with Machine Learning Techniques: Adjusting for Missing Data in Surveys and Data Mining." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1374213636.

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21

Xu, Tianbing. "Nonparametric evolutionary clustering." Diss., Online access via UMI:, 2009.

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22

Zhang, Feng. "Identifying nonlinear variaiton patterns in multivariate manufacturing processes." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1373.

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This dissertation develops a set of nonlinear variation pattern identification methods that are intended to aid in diagnosing the root causes of product variability in complex manufacturing processes, in which large amounts of high dimensional in-process measurement data are collected for quality control purposes. First, a nonlinear variation pattern model is presented to generically represent a single nonlinear variation pattern that results from a single underlying root cause, the nature of which is unknown a priori. We propose a modified version of a principal curve estimation algorithm for

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23

Ke, Yiping. "Efficient correlated pattern discovery in databases /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20KE.

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24

Geltz, Rebecca L. "Using Data Mining to Model Student Success." Youngstown State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1264697709.

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25

Chen, Xi. "Automatic ¹³C Chemical Shift Reference Correction of Protein NMR Spectral Data Using Data Mining and Bayesian Statistical Modeling." UKnowledge, 2019. https://uknowledge.uky.edu/biochem_etds/40.

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Nuclear magnetic resonance (NMR) is a highly versatile analytical technique for studying molecular configuration, conformation, and dynamics, especially of biomacromolecules such as proteins. However, due to the intrinsic properties of NMR experiments, results from the NMR instruments require a refencing step before the down-the-line analysis. Poor chemical shift referencing, especially for 13C in protein Nuclear Magnetic Resonance (NMR) experiments, fundamentally limits and even prevents effective study of biomacromolecules via NMR. There is no available method that can rereference carbon che

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26

Von, Borries George Freitas. "Partition clustering of High Dimensional Low Sample Size data based on P-Values." Diss., Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/590.

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27

Easton, Jonathan. "Mathematical models of health focusing on diabetes : delay differential equations and data mining." Thesis, Northumbria University, 2015. http://nrl.northumbria.ac.uk/23582/.

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Mathematical models have been applied to biology and health to gain a better understanding of physiological systems and disease, as well as to improve levels of treatment and care for certain conditions. This thesis will focus on two different methodologies to investigate models of health, namely delay differential equations andBayesian based data mining. The first approach uses delay differential equations to model the glucose-insulin regulation system. Many models exist in this area, typically including four exponential functions, and take a number of different forms. The model used here is

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28

Wang, Xiaofeng. "New Procedures for Data Mining and Measurement Error Models with Medical Imaging Applications." Case Western Reserve University School of Graduate Studies / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=case1121447716.

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29

Schweickart, Ian R. W. "Investigating Post-Earnings-Announcement Drift Using Principal Component Analysis and Association Rule Mining." Scholarship @ Claremont, 2017. https://scholarship.claremont.edu/hmc_theses/94.

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Post-Earnings-Announcement Drift (PEAD) is commonly accepted in the fields of accounting and finance as evidence for stock market inefficiency. Less accepted are the numerous explanations for this anomaly. This project aims to investigate the cause for PEAD by harnessing the power of machine learning algorithms such as Principle Component Analysis (PCA) and a rule-based learning technique, applied to large stock market data sets. Based on the notion that the market is consumer driven, repeated occurrences of irrational behavior exhibited by traders in response to news events such as earnings r

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30

Michels, Kurt Andrew. "New Statistical Methods and Computational Tools for Mining Big Data, with Applications in Plant Sciences." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/613247.

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The purpose of this dissertation is to develop new statistical tools for mining big data in plant sciences. In particular, the dissertation consists of four inter-related projects to address various methodological and computational challenges in phylogenetic methods. Project 1 aims to systematically test different optimization tools and provide useful strategies to improve optimization in practice. Project 2 develops a new R package rPlant, which provides a friendly and convenient toolbox for users of iPlant. Project 3 presents a fast and effective group-screening method to identify important

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31

Sharif, Abbass. "Visual Data Mining Techniques for Functional Actigraphy Data: An Object-Oriented Approach in R." DigitalCommons@USU, 2012. https://digitalcommons.usu.edu/etd/1394.

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Actigraphy, a technology for measuring a subject's overall activity level almost continuously over time, has gained a lot of momentum over the last few years. An actigraph, a watch-like device that can be attached to the wrist or ankle of a subject, uses an accelerometer to measure human movement every minute or even every 15 seconds. Actigraphy data is often treated as functional data. In this dissertation, we discuss what has been done regarding the visualization of actigraphy data, and then we will explain the three main goals we achieved: (i) develop new multivariate visualization techniqu

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32

Liu, Ye. "Numerical algorithms for data clustering." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/701.

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Data clustering is a process of grouping unlabeled objects based on the imformation describing their relationship. And it has obtained a lot of attentions in data mining for its wide applications in life. For example, in marketing, companys are interested in ﬁnding groups of customers with similar purchase behavior, which will help them to make suitable plans to gain more proﬁts. Besides, in biology, we can make use of data clustering to distinguish planets and animals given their features. Whats more, in earthquake analysis, by clustering observed earthquake epicenters, dangerous area can be

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33

SIMONETTI, Andrea. "Development of statistical methods for the analysis of textual data." Doctoral thesis, Università degli Studi di Palermo, 2022. https://hdl.handle.net/10447/574870.

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34

Quan, Aaron. "Batch Sequencing Methods for Computer Experiments." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1401462464.

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35

Labaš, Dominik. "Analýza metod pro detekci odlehlých hodnot." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445527.

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The topic of this thesis is analysis of methods for detection of outliers. Firstly, a description of outliers and various methods for their detection is provided. Then a description of selected data sets for testing of methods for detection of outliers is given. Next, an application design for the analysis of the described methods is presented. Then, technologies are presented, which provide models for described methods of detection of outliers. The implementation is then described in more detail. Subsequently, the results of experiments are presented, which represent the main part of this the

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36

Ghareeb, Ahmed. "Data mining for University of Dayton campus buildings to predict future demand." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1490472227466522.

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37

Pham, Minh H. "Signal Detection of Adverse Drug Reaction using the Adverse Event Reporting System: Literature Review and Novel Methods." Scholar Commons, 2018. http://scholarcommons.usf.edu/etd/7218.

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One of the objectives of the U.S. Food and Drug Administration is to protect the public health through post-marketing drug safety surveillance, also known as Pharmacovigilance. An inexpensive and efficient method to inspect post-marketing drug safety is to use data mining algorithms on electronic health records to discover associations between drugs and adverse events. The purpose of this study is two-fold. First, we review the methods and algorithms proposed in the literature for identifying association drug interactions to an adverse event and discuss their advantages and drawbacks. Second,

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38

Van, den Honert Andrew. "Estimating the continuous risk of accidents occurring in the South African mining industry." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/96072.

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Thesis (MEng)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: Statistics from mining accidents expose that the potential for injury or death to employees from occupational accidents is relatively high. This study attempts to contribute to the on-going efforts to improve occupational safety in the mining industry by creating a model capable of predicting the continuous risk of occupational accidents occurring. Model inputs include the time of day, time into shift, temperatures, humidity, rainfall and production rate. The approach includes using an Artificial Neural Network (ANN) to i

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39

Ponweiser, Martin. "Latent Dirichlet Allocation in R." WU Vienna University of Economics and Business, 2012. http://epub.wu.ac.at/3558/1/main.pdf.

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Topic models are a new research field within the computer sciences information retrieval and text mining. They are generative probabilistic models of text corpora inferred by machine learning and they can be used for retrieval and text mining tasks. The most prominent topic model is latent Dirichlet allocation (LDA), which was introduced in 2003 by Blei et al. and has since then sparked off the development of other topic models for domain-specific purposes. This thesis focuses on LDA's practical application. Its main goal is the replication of the data analyses from the 2004 LDA paper ``Findi

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Abdulgader, Musbah M. "Bio Inspired Evolutionary Fuzzy System for Data Classification." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1575563281684676.

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41

Low-Kam, Cécile. "Etude probabiliste et statistique des grandes bases de données." Thesis, Montpellier 2, 2010. http://www.theses.fr/2010MON20171.

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Cette thèse se situe à l'interface de la statistique et de la fouille de données. Elle est composée de trois parties indépendantes. Dans la première, nous cherchons à estimer l'ordre (le nombre d'États cachés) d'un modèle de Markov caché dont la distribution d'émission appartient à la famille exponentielle. Nous nous plaçons dans le cas où aucune borne supérieure sur cet ordre n'est connue a priori. Nous définissons deux estimateurs pénalisés pour cet ordre, l'un basé sur le maximum de vraisemblance et l'autre sur une statistique de mélange bayésien. Nous montrons la consistance forte de ces e

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42

Dupal, Pavel. "Statistické metody ve stylometrii." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-359246.

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The aim of this thesis is to provide an overview of some of the commonly used methods in the area of authorship attribution (stylometry). The text begins with a recap of history from the end of the 19th century to present time and the required terminology from the field of text mining is presented and explained. What follows is a list of selected methods from the field of multidimensional statistics (principal components analysis, cluster analysis) and machine learning (Support Vector Machines, Naive Bayes) and their application as pertains to stylometrical problems, including several methods

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Machat, Sebastian. "Implementace Business Intelligence řešení nad daty z provozu parkoviště." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-358773.

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In the world of increasing car sales and limited count of available parking spaces it is hard to imagine the parking space management issues would fade away anytime near. In a simi-lar way the Business Intelligence holds its position as one of the continuous trends in company IT environment. After the parking space navigation systems started to make its first appearance into the public parking lots, it started to be clear that their outputs could be potentially used to gain new information about the way the drivers behave and how the parking space is being used. That is the reason this thesis

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Klasson, Svensson Emil. "Automatic Identification of Duplicates in Literature in Multiple Languages." Thesis, Linköpings universitet, Statistik och maskininlärning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150829.

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As the the amount of books available online the sizes of each these collections are at the same pace growing larger and more commonly in multiple languages. Many of these cor- pora contain duplicates in form of various editions or translations of books. The task of finding these duplicates is usually done manually but with the growing sizes making it time consuming and demanding. The thesis set out to find a method in the field of Text Mining and Natural Language Processing that can automatize the process of manually identifying these duplicates in a corpora mainly consisting of fiction in mul

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Bate, Andrew. "The use of Bayesian confidence propagation neural network in pharmacovigilance." Doctoral thesis, Umeå University, Pharmacology and Clinical Neuroscience, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-83.

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<p>The WHO database contains more than 2.8 million case reports of suspected adverse drug reactions reported from 70 countries worldwide since 1968. The Uppsala Monitoring Centre maintains and analyses this database for new signals on behalf of the WHO Programme for International Drug Monitoring. A goal of the Programme is to detect signals, where a signal is defined as "Reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously."</p><p>The analysis of such a large amount of data on a case by

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46

Wei, Ran. "On Estimation Problems in Network Sampling." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471846863.

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Loscalzo, Steven. "Group based techniques for stable feature selection." Diss., Online access via UMI:, 2009.

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Ruzgys, Martynas. "IT žinių portalo statistikos modulis pagrįstas grupavimu." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2007. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2007~D_20070816_143545-16583.

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Pristatomas duomenų gavybos ir grupavimo naudojimas paplitusiose sistemose bei sukurtas IT žinių portalo statistikos prototipas duomenų saugojimui, analizei ir peržiūrai atlikti. Siūlomas statistikos modulis duomenų saugykloje periodiškais laiko momentais vykdantis duomenų transformacijas. Portale prieinami statistiniai duomenys gali būti grupuoti. Sugrupuotą informaciją pateikus grafiškai, duomenys gali būti interpretuojami ir stebimi veiklos mastai. Panašių objektų grupėms išskirti pritaikytas vienas iš žinomiausių duomenų grupavimo metodų – lygiagretusis k-vidurkių metodas.<br>Presented dat

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Vacula, Vladimír. "Využití statistických metod projektu R v systému pro podporu rozhodování." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2008. http://www.nusl.cz/ntk/nusl-221936.

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The aim of this thesis is to present possibility to integrate Decision Support System with specialized system for statistical computing and provides easier way to analyze economics indicators using sophisticated statistical methods. The R project is complex set of applications, designated for manipulation, computing and graphical presentation of data sets. It is mostly used for statistical analysis and graphical presentations. It allows users to create new methods with language similar to S as well as using the default methods provided.

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Benkovská, Petra. "Web Usage Mining." Master's thesis, Vysoká škola ekonomická v Praze, 2007. http://www.nusl.cz/ntk/nusl-3950.

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General characteristic of web mining including methodology and procedures incorporated into this term. Relation to other areas (data mining, artificial intelligence, statistics, databases, internet technologies, management etc.) Web usage mining - data sources, data pre-processing, characterization of analytical methods and tools, interpretation of outputs (results), and possible areas of usage including examples. Suggestion of solution method, realization and a concrete example's outputs interpretation while using above mentioned methods of web usage mining.

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