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Dissertations / Theses on the topic 'Spatial data mining'

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1

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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2

Yang, Zhao. "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2284.

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Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Bi
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3

Al-Naymat, Ghazi. "NEW METHODS FOR MINING SEQUENTIAL AND TIME SERIES DATA." Thesis, The University of Sydney, 2009. http://hdl.handle.net/2123/5295.

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Data mining is the process of extracting knowledge from large amounts of data. It covers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applications that produce massive amounts of spatial, spatio-temporal (ST) and time series data (TSD) is the rationale for developing specialized techniques to excavate such data. In spatial data mining, the spatial co-location rule problem is different from the
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Al-Naymat, Ghazi. "NEW METHODS FOR MINING SEQUENTIAL AND TIME SERIES DATA." University of Sydney, 2009. http://hdl.handle.net/2123/5295.

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Doctor of Philosophy (PhD)<br>Data mining is the process of extracting knowledge from large amounts of data. It covers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applications that produce massive amounts of spatial, spatio-temporal (ST) and time series data (TSD) is the rationale for developing specialized techniques to excavate such data. In spatial data mining, the spatial co-location rule pr
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5

Koperski, Krzysztof. "A progressive refinement approach to spatial data mining." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0024/NQ51882.pdf.

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6

Yang, Hui. "A general framework for mining spatial and spatio-temporal object association patterns in scientific data." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155319799.

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7

Yu, Ping. "FP-tree Based Spatial Co-location Pattern Mining." Thesis, University of North Texas, 2005. https://digital.library.unt.edu/ark:/67531/metadc4724/.

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A co-location pattern is a set of spatial features frequently located together in space. A frequent pattern is a set of items that frequently appears in a transaction database. Since its introduction, the paradigm of frequent pattern mining has undergone a shift from candidate generation-and-test based approaches to projection based approaches. Co-location patterns resemble frequent patterns in many aspects. However, the lack of transaction concept, which is crucial in frequent pattern mining, makes the similar shift of paradigm in co-location pattern mining very difficult. This thesis investi
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8

SHENCOTTAH, K. N. KALYANKUMAR. "FINDING CLUSTERS IN SPATIAL DATA." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179521337.

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9

Lin, Zhungshan. "Optimal Candidate Generation in Spatial Co-Location Mining." DigitalCommons@USU, 2009. https://digitalcommons.usu.edu/etd/377.

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Existing spatial co-location algorithms based on levels suffer from generating extra, nonclique candidate instances. Thus, they require cliqueness checking at every level. In this thesis, a novel, spatial co-location mining algorithm that automatically generates co-located spatial features without generating any nonclique candidates at any level is proposed. Subsequently, this algorithm generates fewer candidates than other existing level-wise, co-location algorithms without losing any pertinent information. The benefits of this algorithm have been clearly observed at early stages in the mini
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10

Pech, Palacio Manuel Alfredo. "Spatial data modeling and mining using a graph-based representation." Lyon, INSA, 2005. http://theses.insa-lyon.fr/publication/2005ISAL0118/these.pdf.

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Est proposé un unique modèle basé sur des graphes pour représenter des données spatiales, les données non-spatiales et les relations entre les objets spatiaux. Ainsi un graphe est généré à partir de ces trois éléments. On considère que l'outil de fouille de données basé sur les graphes peut découvrir des patterns incluant ces trois éléments, selon trois types de relation spatiale (topologique, cardinale et de distance). Dans notre modèle, les données spatiales, non-spatiales (attributs non-spatiaux), et les relations spatiales représentent une collections d'un ou plusieurs graphes orientés. Le
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11

Pech, Palacio Manuel Alfredo Laurini Robert Tchounikine Anne Sol Martínez David. "Spatial data modeling and mining using a graph-based representation." Villeurbanne : Doc'INSA, 2006. http://docinsa.insa-lyon.fr/these/pont.php?id=pech_palacio.

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Thèse doctorat : Informatique : Villeurbanne, INSA : 2005. Thèse doctorat : Informatique : Universidad de las Américas - Puebla : 2005.<br>Thèse soutenue en co-tutelle. Thèse rédigée en français, en anglais et en espagnol. Titre provenant de l'écran-titre. Bibliogr. p. 174-182.
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12

Kou, Yufeng. "Abnormal Pattern Recognition in Spatial Data." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/30145.

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In the recent years, abnormal spatial pattern recognition has received a great deal of attention from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden but valuable knowledge in many applications. For example, it can help locate extreme meteorological events such as tornadoes and hurricanes, identify aberrant genes or tumor cells, discover highway traffic
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13

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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14

Sips, Mike. "Pixel-based visual data mining in large geo-spatial point sets /." Konstanz : Hartung-Gorre, 2006. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=014881714&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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15

Ruß, Georg [Verfasser], and Rudolf [Akademischer Betreuer] Kruse. "Spatial data mining in precision agriculture / Georg Ruß. Betreuer: Rudolf Kruse." Magdeburg : Universitätsbibliothek, 2012. http://d-nb.info/1047596296/34.

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16

Ruß, Georg Verfasser], and Rudolf [Akademischer Betreuer] [Kruse. "Spatial data mining in precision agriculture / Georg Ruß. Betreuer: Rudolf Kruse." Magdeburg : Universitätsbibliothek, 2012. http://nbn-resolving.de/urn:nbn:de:gbv:ma9:1-820.

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17

Sandell, Anna. "GIS, data mining and wild land fire data within Räddningstjänsten." Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-543.

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<p>Geographical information systems (GIS), data mining and wild land fire would theoretically be suitable to use together. However, would data mining in reality bring out any useful information from wild land fire data stored within a GIS? In this report an investigation is done if GIS and data mining are used within Räddningstjänsten today in some municipalities of the former Skaraborg. The investigation shows that neither data mining nor GIS are used within the investigated municipalities. However, there is an interest in using GIS within the organisations in the future but also some kind of
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18

Isik, Narin. "Fuzzy Spatial Data Cube Construction And Its Use In Association Rule Mining." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606056/index.pdf.

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The popularity of spatial databases increases since the amount of the spatial data that need to be handled has increased by the use of digital maps, images from satellites, video cameras, medical equipment, sensor networks, etc. Spatial data are difficult to examine and extract interesting knowledge<br>hence, applications that assist decision-making about spatial data like weather forecasting, traffic supervision, mobile communication, etc. have been introduced. In this thesis, more natural and precise knowledge from spatial data is generated by construction of fuzzy spatial data cube and extr
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19

Demšar, Urška. "Data mining of geospatial data: combining visual and automatic methods." Doctoral thesis, KTH, School of Architecture and the Built Environment (ABE), 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3892.

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<p>Most of the largest databases currently available have a strong geospatial component and contain potentially useful information which might be of value. The discipline concerned with extracting this information and knowledge is data mining. Knowledge discovery is performed by applying automatic algorithms which recognise patterns in the data.</p><p>Classical data mining algorithms assume that data are independently generated and identically distributed. Geospatial data are multidimensional, spatially autocorrelated and heterogeneous. These properties make classical data mining algorithms in
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20

Bogorny, Vania. "Enhancing spatial association rule mining in geographic databases." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2006. http://hdl.handle.net/10183/7841.

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A técnica de mineração de regras de associação surgiu com o objetivo de encontrar conhecimento novo, útil e previamente desconhecido em bancos de dados transacionais, e uma grande quantidade de algoritmos de mineração de regras de associação tem sido proposta na última década. O maior e mais bem conhecido problema destes algoritmos é a geração de grandes quantidades de conjuntos freqüentes e regras de associação. Em bancos de dados geográficos o problema de mineração de regras de associação espacial aumenta significativamente. Além da grande quantidade de regras e padrões gerados a maioria são
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21

Weitl, Harms Sherri K. "Temporal association rule methodologies for geo-spatial decision support /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3091989.

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22

Li, Xiaohui. "A Language and Visual Interface to Specify Complex Spatial Pattern Mining." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5408/.

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The emerging interests in spatial pattern mining leads to the demand for a flexible spatial pattern mining language, on which easy to use and understand visual pattern language could be built. It is worthwhile to define a pattern mining language called LCSPM to allow users to specify complex spatial patterns. I describe a proposed pattern mining language in this paper. A visual interface which allows users to specify the patterns visually is developed. Visual pattern queries are translated into the LCSPM language by a parser and data mining process can be triggered afterwards. The visual langu
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23

Lee, Ho Young. "Diagnosing spatial variation patterns in manufacturing processes." Diss., Texas A&M University, 2003. http://hdl.handle.net/1969/122.

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24

Goler, Isil. "Pattern Extraction By Using Both Spatial And Temporal Features On Turkish Meteorological Data." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612877/index.pdf.

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With the growth in the size of datasets, data mining has been an important research topic and is receiving substantial interest from both academia and industry for many years. Especially, spatio-temporal data mining, mining knowledge from large amounts of spatio-temporal data, is a highly demanding field because huge amounts of spatio-temporal data are collected in various applications. Therefore, spatio-temporal data mining requires the development of novel data mining algorithms and computational techniques for a successful analysis of large spatio-temporal databases. In this thesis, a spati
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25

Wrede, Fredrik. "An Explorative Parameter Sweep: Spatial-temporal Data Mining in Stochastic Reaction-diffusion Simulations." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-280287.

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Stochastic reaction-diffusion simulations has become an efficient approach for modelling spatial aspects of intracellular biochemical reaction networks. By accounting for intrinsic noise due to low copy number of chemical species, stochastic reaction-diffusion simulations have the ability to more accurately predict and model biological systems. As with many simulations software, exploration of the parameters associated with the model can be needed to yield new knowledge about the underlying system. The exploration can be conducted by executing parameter sweeps for a model. However, with little
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26

BATRA, SHALINI. "DISCOVERY OF CLUSTERS IN SPATIAL DATABASES." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1069701237.

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27

Schubert, Erich. "Generalized and efficient outlier detection for spatial, temporal, and high-dimensional data mining." Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-166938.

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Knowledge Discovery in Databases (KDD) ist der Prozess, nicht-triviale Muster aus großen Datenbanken zu extrahieren, mit dem Ziel, dass diese bisher unbekannt, potentiell nützlich, statistisch fundiert und verständlich sind. Der Prozess umfasst mehrere Schritte wie die Selektion, Vorverarbeitung, Evaluierung und den Analyseschritt, der als Data-Mining bekannt ist. Eine der zentralen Aufgabenstellungen im Data-Mining ist die Ausreißererkennung, das Identifizieren von Beobachtungen, die ungewöhnlich sind und mit der Mehrzahl der Daten inkonsistent erscheinen. Solche seltene Beobachtungen können
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28

Dos, Santos Raimundo Fonseca Jr. "Effective Methods of Semantic Analysis in Spatial Contexts." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/49697.

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With the growing spread of spatial data, exploratory analysis has gained a considerable amount of attention. Particularly in the fields of Information Retrieval and Data Mining, the integration of data points helps uncover interesting patterns not always visible to the naked eye. Social networks often link entities that share places and activities; marketing tools target users based on behavior and preferences; and medical technology combines symptoms to categorize diseases. Many of the current approaches in this field of research depend on semantic analysis, which is good for inferencing and
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Icev, Aleksandar. "DARM distance-based association rule mining." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0506103-132405.

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Leighty, Brian David. "Data Mining for Induction of Adjacency Grammars and Application to Terrain Pattern Recognition." NSUWorks, 2009. http://nsuworks.nova.edu/gscis_etd/212.

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The process of syntactic pattern recognition makes the analogy between the syntax of languages and the structure of spatial patterns. The recognition process is achieved by parsing a given pattern to determine if it is syntactically correct with respect to a defined grammar. The generation of pattern grammars can be a cumbersome process when many objects are involved. This has led to the problem of spatial grammar inference. Current approaches have used genetic algorithms and inductive techniques and have demonstrated limitations. Alternative approaches are needed that produce accurate grammar
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31

Schmid, Klaus Arthur [Verfasser], and Matthias [Akademischer Betreuer] Renz. "Searching and mining in enriched geo-spatial data / Klaus Arthur Schmid ; Betreuer: Matthias Renz." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2016. http://d-nb.info/1122435746/34.

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Franzke, Maximilian [Verfasser], and Matthias [Akademischer Betreuer] Renz. "Querying and mining heterogeneous spatial, social, and temporal data / Maximilian Franzke ; Betreuer: Matthias Renz." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2019. http://d-nb.info/1190563630/34.

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33

Yan, Ping. "SPATIAL-TEMPORAL DATA ANALYTICS AND CONSUMER SHOPPING BEHAVIOR MODELING." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/195232.

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RFID technologies are being recently adopted in the retail space tracking consumer in-store movements. The RFID-collected data are location sensitive and constantly updated as a consumer moves inside a store. By capturing the entire shopping process including the movement path rather than analyzing merely the shopping basket at check-out, the RFID-collected data provide unique and exciting opportunities to study consumer purchase behavior and thus lead to actionable marketing applications.This dissertation research focuses on (a) advancing the representation and management of the RFID-collecte
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34

Du, Xiaoxi. "Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets." [Kent, Ohio] : Kent State University, 2009. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=kent1239139458.

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Thesis (M.S.)--Kent State University, 2009.<br>Title from PDF t.p. (viewed Nov. 13, 2009). Advisor: Ruoming Jin. Keywords: migration motif, trajectory mining, sequential pattern mining, time series clustering. Includes bibliographical references (p. 47-57).
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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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36

Kucuktunc, Onur. "Result Diversification on Spatial, Multidimensional, Opinion, and Bibliographic Data." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1374148621.

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37

Khiali, Lynda. "Fouille de données à partir de séries temporelles d’images satellites." Thesis, Montpellier, 2018. http://www.theses.fr/2018MONTS046/document.

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Les images satellites représentent de nos jours une source d’information incontournable. Elles sont exploitées dans diverses applications, telles que : la gestion des risques, l’aménagent des territoires, la cartographie du sol ainsi qu’une multitude d’autre taches. Nous exploitons dans cette thèse les Séries Temporelles d’Images Satellites (STIS) pour le suivi des évolutions des habitats naturels et semi-naturels. L’objectif est d’identifier, organiser et mettre en évidence des patrons d’évolution caractéristiques de ces zones.Nous proposons des méthodes d’analyse de STIS orientée objets, en o
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Schubert, Erich [Verfasser], and Hans-Peter [Akademischer Betreuer] Kriegel. "Generalized and efficient outlier detection for spatial, temporal, and high-dimensional data mining / Erich Schubert. Betreuer: Hans-Peter Kriegel." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2013. http://d-nb.info/1048522377/34.

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Daniel, Guilherme Priólli [UNESP]. "Otimização de algoritmos de agrupamento espacial baseado em densidade aplicados em grandes conjuntos de dados." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/143832.

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Submitted by Guilherme Priólli Daniel (gui.computacao@yahoo.com.br) on 2016-09-06T13:30:29Z No. of bitstreams: 1 Dissertação_final.pdf: 2456534 bytes, checksum: 4d2279141f7c034de1e4e4e261805db8 (MD5)<br>Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2016-09-09T17:54:56Z (GMT) No. of bitstreams: 1 daniel_gp_me_sjrp.pdf: 2456534 bytes, checksum: 4d2279141f7c034de1e4e4e261805db8 (MD5)<br>Made available in DSpace on 2016-09-09T17:54:56Z (GMT). No. of bitstreams: 1 daniel_gp_me_sjrp.pdf: 2456534 bytes, checksum: 4d2279141f7c034de1e4e4e261805d
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Mendez, Chaves Diego. "A Framework for Participatory Sensing Systems." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4135.

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Participatory sensing (PS) systems are a new emerging sensing paradigm based on the participation of cellular users in a cooperative way. Due to the spatio-temporal granularity that a PS system can provide, it is now possible to detect and analyze events that occur at different scales, at a low cost. While PS systems present interesting characteristics, they also create new problems. Since the measuring devices are cheaper and they are in the hands of the users, PS systems face several design challenges related to the poor accuracy and high failure rate of the sensors, the possibility of malic
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Fu, Kaiqun. "Spatiotemporal Event Forecasting and Analysis with Ubiquitous Urban Sensors." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104165.

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The study of information extraction and knowledge exploration in the urban environment is gaining popularity. Ubiquitous sensors and a plethora of statistical reports provide an immense amount of heterogeneous urban data, such as traffic data, crime activity statistics, social media messages, and street imagery. The development of methods for heterogeneous urban data-based event identification and impacts analysis for a variety of event topics and assumptions is the subject of this dissertation. A graph convolutional neural network for crime prediction, a multitask learning system for traffic
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Zhou, Guoqing. "Co-Location Decision Tree for Enhancing Decision-Making of Pavement Maintenance and Rehabilitation." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/26059.

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A pavement management system (PMS) is a valuable tool and one of the critical elements of the highway transportation infrastructure. Since a vast amount of pavement data is frequently and continuously being collected, updated, and exchanged due to rapidly deteriorating road conditions, increased traffic loads, and shrinking funds, resulting in the rapid accumulation of a large pavement database, knowledge-based expert systems (KBESs) have therefore been developed to solve various transportation problems. This dissertation presents the development of theory and algorithm for a new decision tree
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43

Ågren, Ola. "Finding, extracting and exploiting structure in text and hypertext." Doctoral thesis, Umeå universitet, Institutionen för datavetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-22352.

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Data mining is a fast-developing field of study, using computations to either predict or describe large amounts of data. The increase in data produced each year goes hand in hand with this, requiring algorithms that are more and more efficient in order to find interesting information within a given time. In this thesis, we study methods for extracting information from semi-structured data, for finding structure within large sets of discrete data, and to efficiently rank web pages in a topic-sensitive way. The information extraction research focuses on support for keeping both documentation and
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44

Jguirim, Ines. "Modélisation et génération d'itinéraires contextuels d'activités urbaines dans la ville." Thesis, Brest, 2016. http://www.theses.fr/2016BRES0074/document.

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La ville est une agrégation urbaine permettant d’offrir divers services à ses citadins. Elle constitue un système complexe qui dépend de plusieurs facteurs sociaux et économiques. La configuration de l’espace influence d’une manière importante l’accessibilité aux différentes fonctionnalités de la ville. L’analyse spatiale de la structure urbaine est réalisée sur les villes afin d’étudier les caractéristiques de l’espace et pouvoir évaluer son potentiel fonctionnel. L’enjeu de la thèse est de proposer une approche d’analyse spatiale qui prenne en compte les différents aspects structurels et sém
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Cavazzi, Stefano. "Spatial scale analysis of landscape processes for digital soil mapping in Ireland." Thesis, Cranfield University, 2013. http://dspace.lib.cranfield.ac.uk/handle/1826/8591.

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Soil is one of the most precious resources on Earth because of its role in storing and recycling water and nutrients essential for life, providing a variety of ecosystem services. This vulnerable resource is at risk from degradation by erosion, salinity, contamination and other effects of mismanagement. Information from soil is therefore crucial for its sustainable management. While the demand for soil information is growing, the quantity of data collected in the field is reducing due to financial constraints. Digital Soil Mapping (DSM) supports the creation of geographically referenced soil d
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Remes, J. (Jukka). "Method evaluations in spatial exploratory analyses of resting-state functional magnetic resonance imaging data." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526202228.

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Abstract Resting-state (RS) measurements during functional magnetic resonance imaging (fMRI) have become an established approach for studying spontaneous brain activity. RS-fMRI results are often obtained using explorative approaches like spatial independent component analysis (sICA). These approaches and their software implementations are rarely evaluated extensively or specifically concerning RS-fMRI. Trust is placed in the software that they will work according to the published method descriptions. Many methods and parameters are used despite the lack of test data, and the validity of the u
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47

Zhang, Weimin. "Topics in living cell miultiphoton laser scanning microscopy (MPLSM) image analysis." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4412.

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Multiphoton laser scanning microscopy (MPLSM) is an advanced fluorescence imaging technology which can produce a less noisy microscope image and minimize the damage in living tissue. The MPLSM image in this research is the dehydroergosterol (DHE, a fluorescent sterol which closely mimics those of cholesterol in lipoproteins and membranes) on living cell's plasma membrane area. The objective is to use a statistical image analysis method to describe how cholesterol is distributed on a living cell's membrane. Statistical image analysis methods applied in this research include image segmentation/c
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48

Evans, Ben Richard. "Data-driven prediction of saltmarsh morphodynamics." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/276823.

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Abstract:
Saltmarshes provide a diverse range of ecosystem services and are protected under a number of international designations. Nevertheless they are generally declining in extent in the United Kingdom and North West Europe. The drivers of this decline are complex and poorly understood. When considering mitigation and management for future ecosystem service provision it will be important to understand why, where, and to what extent decline is likely to occur. Few studies have attempted to forecast saltmarsh morphodynamics at a system level over decadal time scales. There is no synthesis of existing
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49

Prananto, Agnes Kristina. "The use of remotely sensed data to analyse spatial and temporal trends in vegetation patchiness within rehabilitated bauxite mines in the Darling Range, W.A. /." Connect to this title, 2005. http://theses.library.uwa.edu.au/adt-WU2006.0012.

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50

Da, Silva Sébastien. "Fouille de données spatiales et modélisation de linéaires de paysages agricoles." Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0156/document.

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Abstract:
Cette thèse s'inscrit dans un partenariat entre l'INRA et l'INRIA et dans le champs de l'extraction de connaissances à partir de bases de données spatiales. La problématique porte sur la caractérisation et la simulation de paysages agricoles. Plus précisément, nous nous concentrons sur des lignes qui structurent le paysage agricole, telles que les routes, les fossés d'irrigation et les haies. Notre objectif est de modéliser les haies en raison de leur rôle dans de nombreux processus écologiques et environnementaux. Nous étudions les moyens de caractériser les structures de haies sur deux paysa
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