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Dissertations / Theses on the topic 'Data mining. Computer Science'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Taylor, Phillip. "Data mining of vehicle telemetry data." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/77645/.

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Driving a safety critical task that requires a high level of attention and workload from the driver. Despite this, people often perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. As well as these distractions, the driver may also be overloaded for other reasons, such as dealing with an incident on the road or holding conversations in the car. One solution to this distraction problem is to limit the functionality of in-car devices while the driver is overloaded. This can tak

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2

Ghoting, Amol. "Memory- and knowledge-conscious data mining." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1186979749.

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3

Wang, Grant J. (Grant Jenhorn) 1979. "Algorithms for data mining." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38315.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.<br>Includes bibliographical references (p. 81-89).<br>Data of massive size are now available in a wide variety of fields and come with great promise. In theory, these massive data sets allow data mining and exploration on a scale previously unimaginable. However, in practice, it can be difficult to apply classic data mining techniques to such massive data sets due to their sheer size. In this thesis, we study three algorithmic problems in data mining with consideration to the ana

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4

Tziatzios, Achilleas. "Data mining of range-based classification rules for data characterization." Thesis, Cardiff University, 2014. http://orca.cf.ac.uk/65902/.

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Advances in data gathering have led to the creation of very large collections across different fields like industrial site sensor measurements or the account statuses of a financial institution's clients. The ability to learn classification rules, rules that associate specific attribute values with a specific class label, from this data is important and useful in a range of applications. While many methods to facilitate this task have been proposed, existing work has focused on categorical datasets and very few solutions that can derive classification rules of associated continuous ranges (num

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5

Liu, Tantan. "Data Mining over Hidden Data Sources." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343313341.

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6

Zhang, Nan. "Privacy-preserving data mining." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1080.

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7

Al-Hashemi, Idrees Yousef. "Applying data mining techniques over big data." Thesis, Boston University, 2013. https://hdl.handle.net/2144/21119.

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Thesis (M.S.C.S.) PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>The rapid development of information technology in recent decades means that data appear in a wide variety of formats — sensor data, tweets, photographs, raw data, and unstructured data. Statistics show that there were 800,000

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8

Gu, Zhuoer. "Mining previously unknown patterns in time series data." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/99207/.

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The emerging importance of distributed computing systems raises the needs of gaining a better understanding of system performance. As a major indicator of system performance, analysing CPU host load helps evaluate system performance in many ways. Discovering similar patterns in CPU host load is very useful since many applications rely on the pattern mined from the CPU host load, such as pattern-based prediction, classification and relative rule mining of CPU host load. Essentially, the problem of mining patterns in CPU host load is mining the time series data. Due to the complexity of the prob

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9

Mao, Shihong. "Comparative Microarray Data Mining." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1198695415.

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10

Zou, Beibei 1974. "Data mining with relational database management systems." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82456.

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With the increasing demands of transforming raw data into information and knowledge, data mining becomes an important field to the discovery of useful information and hidden patterns in huge datasets. Both machine learning and database research have made major contributions to the field of data mining. However, there is still little effort made to improve the scalability of algorithms applied in data raining tasks. Scalability is crucial for data mining algorithms, since they have to handle large datasets quite often. In this thesis we take a step in this direction by extending a popula

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11

Ma, Xuesong 1975. "Data mining using relational database management system." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98757.

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With the wide availability of huge amounts of data and the imminent demands to transform the raw data into useful information and knowledge, data mining has become an important research field both in the database area and the machine learning areas. Data mining is defined as the process to solve problems by analyzing data already present in the database and discovering knowledge in the data. Database systems provide efficient data storage, fast access structures and a wide variety of indexing methods to speed up data retrieval. Machine learning provides theory support for most of the popular d

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12

Wang, Guan. "Graph-Based Approach on Social Data Mining." Thesis, University of Illinois at Chicago, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3668648.

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<p> Powered by big data infrastructures, social network platforms are gathering data on many aspects of our daily lives. The online social world is reflecting our physical world in an increasingly detailed way by collecting people's individual biographies and their various of relationships with other people. Although massive amount of social data has been gathered, an urgent challenge remain unsolved, which is to discover meaningful knowledge that can empower the social platforms to really understand their users from different perspectives. </p><p> Motivated by this trend, my research addres

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13

Kamra, Varun. "Mining discriminating patterns in data with confidence." Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10196147.

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<p> There are many pattern mining algorithms available for classifying data. The main drawback of most of the algorithms is that they always focus on mining frequent patterns in data that may not always be discriminative enough for classification. There could exist patterns that are not frequent, but are efficient discriminators. In such cases these algorithms might not perform well. This project proposes the MDP algorithm, which aims to search for patterns that are good at discriminating between classes rather than searching for frequent patterns. The MDP ensures that there is at least one mo

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14

Brindley, Paul. "Generating vague geographic information through data mining of passive web data." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/33722/.

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Vagueness is an inherent property of geographic data. This thesis develops a geocomputational method that demonstrates that vague information has the potential to be incorporated within GIS in straightforward manner. This method applies vagueness to the elements of place: types, names and spatial boundaries, generating vague geographic objects by extracting and filtering the differing opinions and perceptions held within web derived data. The aim of the research is threefold: (1) to investigate an approach to automatically generate vague, probabilistic geographical information concerning place

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15

Huck, Kevin A. "Knowledge support for parallel performance data mining /." Connect to title online (Scholars' Bank) Connect to title online (ProQuest), 2009. http://hdl.handle.net/1794/10087.

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16

Chen, Yu 1979. "Data mining relational databases with probabilistic relational models." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97928.

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Relational databases are a popular method for organizing and storing data. Unfortunately, many machine-learning techniques are unable to handle complex relational models. The Probabilistic Relational Model (PRM) is an extension of the Bayesian Network framework that can express relational structure as well as probabilistic dependencies. In this thesis, we significantly expand and improve an implementation of PRMs that allows defining conditional probability distributions over discrete and continuous variables. The thesis uses as starting point an implementation that has various problems, and r

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17

Muralidharan, Sinduja. "Evaluation of Fermi Features for Data Mining Algorithms." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1308254873.

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18

Pena, Isis. "Utility-based data mining: An anthropometric case study." Thesis, University of Ottawa (Canada), 2008. http://hdl.handle.net/10393/27723.

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One of the most important challenges for the apparel industry is to produce garments that fit the population properly. In order to achieve this objective, it is crucial to understand the typical profile of consumer's bodies. In this work, we aim to identify the typical consumer from the virtual tailor's perspective. To this end, we perform clustering analysis on anthropometric and 3-D data to group the population into clothing sizes. Next, we perform multi-view relational classification to analyze the interplay of different body measurements within each size. In this study, we analyze three di

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19

Anderson, Alec W. (Alec Wayne). "Deep Mining : scaling Bayesian auto-tuning of data science pipelines." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/119509.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 105-108).<br>Within the automated machine learning movement, hyperparameter optimization has emerged as a particular focus. Researchers have introduced various search algorithms and open-source systems in order to automatica

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20

Du, Xiaoshan. "Data Mining Analysis and Modeling for Marketing Based on Attributes of Customer Relationship." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-865.

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<p>With the rapid growing marketing business, Data Mining technology is playing a more and more important role in the demands of analyzing and utilizing the large scale information gathered from customers. To predict the consequent business strategy by using Data Mining, the Customer Relationship Management (CRM) nowadays is required to evaluate the customer performance, discover the trends or patterns in customer behavior, and understand the factual value of their customers to their company. In this paper, we present an effective model to apply Data Mining to the CRM problem of categorizing t

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21

JANAKIRAMAN, KRISHNAMOORTHY. "ENTITY IDENTIFICATION USING DATA MINING TECHNIQUES." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin989852516.

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22

Sherikar, Vishnu Vardhan Reddy. "I2MAPREDUCE: DATA MINING FOR BIG DATA." CSUSB ScholarWorks, 2017. https://scholarworks.lib.csusb.edu/etd/437.

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This project is an extension of i2MapReduce: Incremental MapReduce for Mining Evolving Big Data . i2MapReduce is used for incremental big data processing, which uses a fine-grained incremental engine, a general purpose iterative model that includes iteration algorithms such as PageRank, Fuzzy-C-Means(FCM), Generalized Iterated Matrix-Vector Multiplication(GIM-V), Single Source Shortest Path(SSSP). The main purpose of this project is to reduce input/output overhead, to avoid incurring the cost of re-computation and avoid stale data mining results. Finally, the performance of i2MapReduce is anal

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23

Su, Wei. "Motif Mining On Structured And Semi-structured Biological Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1365089538.

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24

Fukuda, Kyoko. "Computer-Enhanced Knowledge Discovery in Environmental Science." Thesis, University of Canterbury. Mathematics and Statistics, 2009. http://hdl.handle.net/10092/2140.

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Encouraging the use of computer algorithms by developing new algorithms and introducing uncommonly known algorithms for use on environmental science problems is a significant contribution, as it provides knowledge discovery tools to extract new aspects of results and draw new insights, additional to those from general statistical methods. Conducting analysis with appropriately chosen methods, in terms of quality of performance and results, computation time, flexibility and applicability to data of various natures, will help decision making in the policy development and management process for e

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25

Albashiri, Kamal Ali. "An investigation into the issues of multi-agent data mining." Thesis, University of Liverpool, 2010. http://livrepository.liverpool.ac.uk/1275/.

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Very often data relevant to one search is not located at a single site, it may be widely-distributed and in many different forms. Similarly there may be a number of algorithms that may be applied to a single Knowledge Discovery in Databases (KDD) task with no obvious “best” algorithm. There is a clear advantage to be gained from a software organisation that can locate, evaluate, consolidate and mine data from diverse sources and/or apply a diverse number of algorithms. Multi-agent systems (MAS) often deal with complex applications that require distributed problem solving. Since MAS are often d

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26

Yang, Lusann Wren. "Data Mining Chemistry and Crystal Structure." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11454.

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The availability of large amounts of data generated by high-throughput computing and experimentation has generated interest in the application of machine learning techniques to materials science. Machine learning of materials behavior requires the use of feature vectors that capture compositional or structural information influence a target property. We present methods for assessing the similarity of compositions, substructures, and crystal structures. Similarity measures are important for the classification and clustering of data points, allowing for the organization of data and the predicti

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27

Nguyen, Thuy Thi Thu. "Predicting cardiovascular risks using pattern recognition and data mining." Thesis, University of Hull, 2009. http://hydra.hull.ac.uk/resources/hull:3051.

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This thesis presents the use of pattern recognition and data mining techniques into risk prediction models in the clinical domain of cardiovascular medicine. The data is modelled and classified by using a number of alternative pattern recognition and data mining techniques in both supervised and unsupervised learning methods. Specific investigated techniques include multilayer perceptrons, radial basis functions, and support vector machines for supervised classification, and self organizing maps, KMIX and WKMIX algorithms for unsupervised clustering. The Physiological and Operative Severity Sc

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28

Bouret, Megan Sue. "A data mining approach for identifying pavement distress signatures." Thesis, North Dakota State University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=1606201.

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<p> This work introduces signature-based data mining of pavement distress data. The goal is to understand the factors that influence pavement distress. The presented approach maintains multiple types of flexible pavement distress scores throughout the analysis and considers them as signatures. The signatures are used to establish the relationship between distress score increases and overweight truck characteristics. Hierarchical clustering of pavement distress signatures provides insights into similarities among road segments. The use of signatures, rather than composite distress scores, is co

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Zhang, Xin Iris, and 張欣. "Fast mining of spatial co-location patterns." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30462708.

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30

Aldana, Walter Alberto 1977. "Data mining industry : emerging trends and new opportunities." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/81535.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, June 2000.<br>"May 2000."<br>Includes bibliographical references (leaves 170-179).<br>by Walter Alberto Aldana.<br>M.Eng.

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Qaiser, Elizae. "Quantization of Real-Valued Attributes for Data Mining." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin983500840.

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32

Abedjan, Ziawasch. "Improving RDF data with data mining." Phd thesis, Universität Potsdam, 2014. http://opus.kobv.de/ubp/volltexte/2014/7133/.

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Linked Open Data (LOD) comprises very many and often large public data sets and knowledge bases. Those datasets are mostly presented in the RDF triple structure of subject, predicate, and object, where each triple represents a statement or fact. Unfortunately, the heterogeneity of available open data requires significant integration steps before it can be used in applications. Meta information, such as ontological definitions and exact range definitions of predicates, are desirable and ideally provided by an ontology. However in the context of LOD, ontologies are often incomplete or simply not

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33

Mahamaneerat, Wannapa Kay Shyu Chi-Ren. "Domain-concept mining an efficient on-demand data mining approach /." Diss., Columbia, Mo. : University of Missouri--Columbia, 2008. http://hdl.handle.net/10355/7195.

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Title from PDF of title page (University of Missouri--Columbia, viewed on February 24, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. Chi-Ren Shyu. Vita. Includes bibliographical references.

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34

Ma, Yao 1975. "Data warehousing, OLAP, and data mining : an integrated strategy for use at FAA." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/47590.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.<br>Includes bibliographical references (leaves 76-77).<br>by Yao Ma.<br>S.B.and M.Eng.

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35

Abu, Mansour Hussein Y. "Rule pruning and prediction methods for associative classification approach in data mining." Thesis, University of Huddersfield, 2012. http://eprints.hud.ac.uk/id/eprint/17476/.

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Recent studies in data mining revealed that Associative Classification (AC) data mining approach builds competitive classification classifiers with reference to accuracy when compared to classic classification approaches including decision tree and rule based. Nevertheless, AC algorithms suffer from a number of known defects as the generation of large number of rules which makes it hard for end-user to maintain and understand its outcome and the possible over-fitting issue caused by the confidence-based rule evaluation used by AC. This thesis attempts to deal with above problems by presenting

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36

Palmer, Nathan Patrick. "Data mining techniques for large-scale gene expression analysis." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68493.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 238-256).<br>Modern computational biology is awash in large-scale data mining problems. Several high-throughput technologies have been developed that enable us, with relative ease and little expense, to evaluate the coordinated expression levels of tens of thousands of genes, evaluate hundreds of thousands of single-nucleotide polymorphisms, and sequence individual genomes. The data produced by th

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Stauffer, Christopher P. (Christopher Paul) 1971. "Perceptual data mining : bootstrapping visual intelligence from tracking behavior." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8111.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.<br>Includes bibliographical references (p. 161-166).<br>One common characteristic of all intelligent life is continuous perceptual input. A decade ago, simply recording and storing a a few minutes of full frame-rate NTSC video required special hardware. Today, an inexpensive personal computer can process video in real-time tracking and recording information about multiple objects for extended periods of time, which fundamentally enables this research. This thesis is about Percept

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Mehta, Sameep. "Realizing a feature-based framework for scientific data mining." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155650127.

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39

Rahman, M. Mostafizur. "Machine learning based data pre-processing for the purpose of medical data mining and decision support." Thesis, University of Hull, 2014. http://hydra.hull.ac.uk/resources/hull:10103.

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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. Sometimes, improved data quality is itself the goal of the analysis, usually to improve processes in a production database and the designing of decision support. As medicine moves forward there is a need for sophisticated decision support systems that make use of data mining to support more orthodox knowledge engineering and Health Informatics practice. However, the real-life medical data rarely complies with the requirement

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HE, AIJING. "UNSUPERVISED DATA MINING BY RECURSIVE PARTITIONING." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1026406153.

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41

Huck, Kevin A. 1972. "Knowledge support for parallel performance data mining." Thesis, University of Oregon, 2009. http://hdl.handle.net/1794/10087.

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xvi, 231 p. : ill. A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number.<br>Parallel applications running on high-end computer systems manifest a complex combination of performance phenomena, such as communication patterns, work distributions, and computational inefficiencies. Current performance tools compute results that help to describe performance behavior, as well as to understand performance problems and how they came about. Unfortunately, parallel performance tool research has been limited in its contributions

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42

Jiang, Chunyu. "DATA MINING AND ANALYSIS ON MULTIPLE TIME SERIES OBJECT DATA." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1177959264.

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43

Lan, Liang. "Data Mining Algorithms for Classification of Complex Biomedical Data." Diss., Temple University Libraries, 2012. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/214773.

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Computer and Information Science<br>Ph.D.<br>In my dissertation, I will present my research which contributes to solve the following three open problems from biomedical informatics: (1) Multi-task approaches for microarray classification; (2) Multi-label classification of gene and protein prediction from multi-source biological data; (3) Spatial scan for movement data. In microarray classification, samples belong to several predefined categories (e.g., cancer vs. control tissues) and the goal is to build a predictor that classifies a new tissue sample based on its microarray measurements. When

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McCallen, Scott J. "Mining Dynamic Structures in Complex Networks." Kent State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=kent1204154279.

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Hawana, Leila Mohammed. "Knowing Without Knowing: Real-Time Usage Identification of Computer Systems." PDXScholar, 2019. https://pdxscholar.library.pdx.edu/open_access_etds/4680.

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Contemporary computers attempt to understand a user's actions and preferences in order to make decisions that better serve the user. In pursuit of this goal, computers can make observations that range from simple pattern recognition to listening in on conversations without the device being intentionally active. While these developments are incredibly useful for customization, the inherent security risks involving personal data are not always worth it. This thesis attempts to tackle one issue in this domain, computer usage identification, and presents a solution that identifies high-level usage

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Korkmaz, Gulberal Kircicegi Yoksul. "Mining Microarray Data For Biologically Important Gene Sets." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614266/index.pdf.

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Microarray technology enables researchers to measure the expression levels of thousands of genes simultaneously to understand relationships between genes, extract pathways, and in general understand a diverse amount of biological processes such as diseases and cell cycles. While microarrays provide the great opportunity of revealing information about biological processes, it is a challenging task to mine the huge amount of information contained in the microarray datasets. Generally, since an accurate model for the data is missing, first a clustering algorithm is applied and then the resulting

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Baker, Frazier N. "Mining and Visualization of Amino Acid Coevolution Data." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1571061614939124.

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Li, Zhizhou. "Multi-Scheme Fully Homomorphic Encryptions And Its Application In Privacy Preserving Data Mining." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1430760068.

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Gondek, David. "Non-redundant clustering /." View online version; access limited to Brown University users, 2005. http://wwwlib.umi.com/dissertations/fullcit/3174612.

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Liu, Ru Xuan. "Link prediction in time-aware graphs." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691176.

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