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Titre : Spectral Feature Selection for Data Mining Type de document : Monographie Auteurs : Zheng Alan Zhao, Auteur ; Huan Liu, Auteur Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2011 Importance : 224 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-0-429-10719-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multivariée
[Termes IGN] analyse spectrale
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] corrélation à l'aide de traits caractéristiques
[Termes IGN] exploration de données
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] traitement de donnéesRésumé : (éditeur)Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervised feature selection.
The book explores the latest research achievements, sheds light on new research directions, and stimulates readers to make the next creative breakthroughs. It presents the intrinsic ideas behind spectral feature selection, its theoretical foundations, its connections to other algorithms, and its use in handling both large-scale data sets and small sample problems. The authors also cover feature selection and feature extraction, including basic concepts, popular existing algorithms, and applications.
A timely introduction to spectral feature selection, this book illustrates the potential of this powerful dimensionality reduction technique in high-dimensional data processing. Readers learn how to use spectral feature selection to solve challenging problems in real-life applications and discover how general feature selection and extraction are connected to spectral feature selection.Note de contenu : 1- Data of High Dimensionality and Challenges
2- Univariate Formulations for Spectral Feature Selection
3- Multivariate Formulations
4- Connections to Existing Algorithms
5- Large-Scale Spectral Feature Selection
6- Multi-Source Spectral Feature SelectionNuméro de notice : 25844 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Monographie En ligne : https://www.taylorfrancis.com/books/9780429107191 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95251 TIDES, a new descriptor for time series oscillation behavior / L. Mariote in Geoinformatica, vol 15 n° 1 (January 2011)
[article]
Titre : TIDES, a new descriptor for time series oscillation behavior Type de document : Article/Communication Auteurs : L. Mariote, Auteur ; C. Medeiros, Auteur ; Ricardo Da Silva Torres, Auteur ; et al., Auteur Année de publication : 2011 Article en page(s) : pp 75 - 109 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] exploration de données géographiques
[Termes IGN] oscillation
[Termes IGN] série temporelleRésumé : (Auteur) Sensor networks have increased the amount and variety of temporal data available, requiring the definition of new techniques for data mining. Related research typically addresses the problems of indexing, clustering, classification, summarization, and anomaly detection. There is a wide range of techniques to describe and compare time series, but they focus on series’ values. This paper concentrates on a new aspect—that of describing oscillation patterns. It presents a technique for time series similarity search, and multiple temporal scales, defining a descriptor that uses the angular coefficients from a linear segmentation of the curve that represents the evolution of the analyzed series. This technique is generalized to handle co-evolution, in which several phenomena vary at the same time. Preliminary experiments with real datasets showed that our approach correctly characterizes the oscillation of single time series, for multiple time scales, and is able to compute the similarity among sets of co-evolving series. Numéro de notice : A2011-029 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-010-0112-5 Date de publication en ligne : 18/06/2010 En ligne : https://doi.org/10.1007/s10707-010-0112-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30810
in Geoinformatica > vol 15 n° 1 (January 2011) . - pp 75 - 109[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-2011011 RAB Revue Centre de documentation En réserve L003 Disponible vol 24 n° 10 - october 2010 - Geospatial visual analytics : focus on time. Special issue of the ICA commission on geovisualization (Bulletin de International journal of geographical information science IJGIS) / Gennady Adrienko
[n° ou bulletin]
Titre : vol 24 n° 10 - october 2010 - Geospatial visual analytics : focus on time. Special issue of the ICA commission on geovisualization Type de document : Périodique Auteurs : Gennady Adrienko, Éditeur scientifique ; Natalia Adrienko, Éditeur scientifique ; Jason Dykes, Éditeur scientifique ; Menno-Jan Kraak, Éditeur scientifique ; Heidrun Schumann, Éditeur scientifique Année de publication : 2010 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse spatio-temporelle
[Termes IGN] analyse visuelle
[Termes IGN] exploration de données géographiquesNuméro de notice : 079-201006 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Numéro de périodique Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=13726 [n° ou bulletin]Contient
- Assessing the quality of geoscientific simulation models with visual analytics methods: a design study / D. Dransch in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
- Using space-time visual analytic methods for exploring the dynamics of ethnic groups' residential patterns / I. Omer in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
- Visualization of attributed hierarchical structures in a spatiotemporal context / S. Hadlak in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
- Analysing spatio-temporal autocorrelation with LISTA-Viz / F. Hardisty in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
- An integrated approach for visual analysis of a multisource moving objects knowledge base / N. Wllems in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
- Space-time density of trajectories : exploring spatio-temporal patterns in movement data / Urška Demšar in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
- Exploring the efficiency of users' visual analytics strategies based on sequence analysis of eye movement recordings / Arzu Çöltekin in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
- Space, time and visual analytics / Gennady Adrienko in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
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Code-barres Cote Support Localisation Section Disponibilité 079-2010061 RAB Revue Centre de documentation En réserve L003 Disponible 079-2010062 RAB Revue Centre de documentation En réserve L003 Disponible Space-time density of trajectories : exploring spatio-temporal patterns in movement data / Urška Demšar in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
[article]
Titre : Space-time density of trajectories : exploring spatio-temporal patterns in movement data Type de document : Article/Communication Auteurs : Urška Demšar, Auteur ; K. Virrantaus, Auteur Année de publication : 2010 Article en page(s) : pp 1527 - 1542 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] densité
[Termes IGN] données localisées 3D
[Termes IGN] données spatiotemporelles
[Termes IGN] estimation par noyau
[Termes IGN] exploration de données géographiques
[Termes IGN] Finlande
[Termes IGN] navire
[Termes IGN] reconstruction d'itinéraire ou de trajectoire
[Termes IGN] trajectoire (véhicule non spatial)Résumé : (Auteur) Modern positioning and identification technologies enable tracking of almost any type of moving object. A remarkable amount of new trajectory data is thus available for the analysis of various phenomena. In cartography, a typical way to visualise and explore such data is to use a space-time cube, where trajectories are shown as 3D polylines through space and time. With increasingly large movement datasets becoming available, this type of display quickly becomes cluttered and unclear. In this article, we introduce the concept of 3D space-time density of trajectories to solve the problem of cluttering in the space-time cube. The space-time density is a generalisation of standard 2D kernel density around 2D point data into 3D density around 3D polyline data (i.e. trajectories). We present the algorithm for space-time density, test it on simulated data, show some basic visualisations of the resulting density volume and observe particular types of spatio-temporal patterns in the density that are specific to trajectory data. We also present an application to real-time movement data, that is, vessel movement trajectories acquired using the Automatic Identification System (AIS) equipment on ships in the Gulf of Finland. Finally, we consider the wider ramifications to spatial analysis of using this novel type of spatio-temporal visualisation. Numéro de notice : A2010-466 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2010.511223 En ligne : https://doi.org/10.1080/13658816.2010.511223 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30659
in International journal of geographical information science IJGIS > vol 24 n° 10 (october 2010) . - pp 1527 - 1542[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2010061 RAB Revue Centre de documentation En réserve L003 Disponible 079-2010062 RAB Revue Centre de documentation En réserve L003 Disponible Using clustering methods in geospatial information systems / X. Wang in Geomatica, vol 64 n° 3 (September 2010)
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Titre : Using clustering methods in geospatial information systems Type de document : Article/Communication Auteurs : X. Wang, Auteur ; Jing Wang, Auteur Année de publication : 2010 Article en page(s) : pp 347 - 361 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse spatiale
[Termes IGN] attribut sémantique
[Termes IGN] distance euclidienne
[Termes IGN] exploration de données géographiques
[Termes IGN] modèle conceptuel de données localisées
[Termes IGN] regroupement de données
[Termes IGN] système d'information géographique
[Termes IGN] test de performanceRésumé : (Auteur) Spatial clustering is the process of grouping similar objects based on their distance, connectivity, or rel-ative density in space. It has been employed in the field of spatial analysis for years. In order to select the prop-er spatial clustering methods for geospatial information systems, we need to consider the characteristics of different clustering methods, relative to the objectives that we are trying to achieve. In this paper, we give a detailed discussion of different types of clustering methods from a data mining perspective. Analysis of the advantages and limitations of some classical clustering methods are given. Subsequently we discuss applying spatial clustering methods as part of geospatial information systems, with respect to distance functions, data models, non-spatial attributes and performance. Numéro de notice : A2010-529 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.5623/geomat-2010-0035 En ligne : https://doi.org/10.5623/geomat-2010-0035 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30721
in Geomatica > vol 64 n° 3 (September 2010) . - pp 347 - 361[article]Réservation
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Lam in Cartography and Geographic Information Science, vol 29 n° 1 (January 2002)PermalinkL'appariement pour la constitution de bases de données géographiques multi-résolutions / David Sheeren (2002)PermalinkGeospatial theory, processing and applications : proceedings of the ISPRS commission 4 symposium, 8 -12 July 2002, Ottawa, Canada / Costas Armenakis (2002)PermalinkWorkshop 12 Knowledge discovery from temporal and spatial data / Christophe Dousson (2002)PermalinkMaps as social constructions: Power, communication and visualization / Jeremy W. Crampton in Progress in human geography, vol 25 n° 2 (Juin 2001)PermalinkTemporal, spatial, and spatio-temporal data mining / J.F. Roddick (2001)PermalinkThe integration of geographic visualization with knowledge discovery in databases and geocomputation / M. Gahegan in Cartography and Geographic Information Science, vol 28 n° 1 (January 2001)PermalinkJoin indices as a tool for spatial data mining / Karine Zeitouni (12/09/2000)PermalinkLe data mining spatial : une nouvelle voie pour les SIG ? / Françoise de Blomac in SIG la lettre, n° 17 (mai 2000)PermalinkLe data mining spatial et les bases de données spatiales / Karine Zeitouni (2000)PermalinkPermalinkPermalinkGeographical domain and geographical information systems / Stephan Winter (2000)PermalinkJournées data mining spatial et analyse du risque, Versailles, 24 - 25 février 2000 / Sylvain Lassarre (2000)PermalinkRecent issues on fuzzy databases / G. Bordonia (2000)PermalinkLe data mining spatial et les bases de données spatiales / Karine Zeitouni in Revue internationale de géomatique, vol 9 n° 4 (décembre 1999- février 2000)Permalink3D shape histograms for similarity search and classification in spatial databases / M. 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