Descripteur
Documents disponibles dans cette catégorie (40)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Co-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data / Xiaojing Wu in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)
[article]
Titre : Co-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data Type de document : Article/Communication Auteurs : Xiaojing Wu, Auteur ; Raul Zurita-Milla, Auteur ; Menno-Jan Kraak, Auteur Année de publication : 2015 Article en page(s) : pp 624 - 642 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse spatio-temporelle
[Termes IGN] exploration de données géographiques
[Termes IGN] regroupement de données
[Termes IGN] série temporelle
[Termes IGN] température de l'air
[Termes IGN] visualisation de donnéesRésumé : (Auteur) Clustering allows considering groups of similar data elements at a higher level of abstraction. This facilitates the extraction of patterns and useful information from large amounts of spatio-temporal data. Till now, most studies have focused on the extraction of patterns from a spatial or a temporal aspect. Here we use the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) to enable the simultaneous analysis of spatial and temporal patterns in geo-referenced time series (time evolving values of a property observed at fixed geographical locations). In addition, we present three geovisualization techniques to fully explore the co-clustering results: heatmaps offer a straightforward overview of the results; small multiples display the spatial and temporal patterns in geographic maps; ringmaps illustrate the temporal patterns associated to cyclic timestamps. To illustrate this study, we used Dutch daily average temperature data collected at 28 weather stations from 1992 to 2011. The co-clustering algorithm was applied hierarchically to understand the spatio-temporal patterns found in the data at the yearly, monthly and daily resolutions. Results pointed out that there is a transition in temperature patterns from northeast to southwest and from ‘cold’ to ‘hot’ years/months/days with only 3 years belonging to ‘cool’ or ‘cold’ years. Because of its characteristics, this newly introduced algorithm can concurrently analyse spatial and temporal patterns by identifying location-timestamp co-clusters that contain values that are similar along both the spatial and the temporal dimensions. Numéro de notice : A2015-590 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.994520 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.994520 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77876
in International journal of geographical information science IJGIS > vol 29 n° 4 (April 2015) . - pp 624 - 642[article]A polygon-based clustering and analysis framework for mining spatial datasets / Sujing Wang in Geoinformatica, vol 18 n° 3 (July 2014)
[article]
Titre : A polygon-based clustering and analysis framework for mining spatial datasets Type de document : Article/Communication Auteurs : Sujing Wang, Auteur ; Christoph F. Eick, Auteur Année de publication : 2014 Article en page(s) : pp 569 - 594 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] exploration de données géographiques
[Termes IGN] polygone
[Termes IGN] regroupement de donnéesRésumé : (Auteur) Polygons provide natural representations for many types of geospatial objects, such as countries, buildings, and pollution hotspots. Thus, polygon-based data mining techniques are particularly useful for mining geospatial datasets. In this paper, we propose a polygon-based clustering and analysis framework for mining multiple geospatial datasets that have inherently hidden relations. In this framework, polygons are first generated from multiple geospatial point datasets by using a density-based contouring algorithm called DCONTOUR. Next, a density-based clustering algorithm called Poly-SNN with novel dissimilarity functions is employed to cluster polygons to create meta-clusters of polygons. Finally, post-processing analysis techniques are proposed to extract interesting patterns and user-guided summarized knowledge from meta-clusters. These techniques employ plug-in reward functions that capture a domain expert's notion of interestingness to guide the extraction of knowledge from meta-clusters. The effectiveness of our framework is tested in a real-world case study involving ozone pollution events in Texas. The experimental results show that our framework can reveal interesting relationships between different ozone hotspots represented by polygons; it can also identify interesting hidden relations between ozone hotspots and several meteorological variables, such as outdoor temperature, solar radiation, and wind speed. Numéro de notice : A2014-501 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-013-0190-2 Date de publication en ligne : 30/08/2013 En ligne : https://doi.org/10.1007/s10707-013-0190-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74093
in Geoinformatica > vol 18 n° 3 (July 2014) . - pp 569 - 594[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 057-2014031 RAB Revue Centre de documentation En réserve L003 Disponible Band grouping versus band clustering in SVM ensemble classification of hyperspectral imagery / Behnaz Bigdeli in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 6 (June 2013)
[article]
Titre : Band grouping versus band clustering in SVM ensemble classification of hyperspectral imagery Type de document : Article/Communication Auteurs : Behnaz Bigdeli, Auteur ; Farhad Samadzadegan, Auteur ; Peter Reinartz, Auteur Année de publication : 2013 Article en page(s) : pp 523 - 533 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image hyperspectrale
[Termes IGN] regroupement de donnéesRésumé : (Auteur) Due to the dense sampling of spectral signatures of land covers, hyperspectral images have a better discrimination among similar ground cover classes than traditional remote sensing data. However, these images are usually composed of tens or hundreds of spectrally close bands, which result in high redundancy and great amount of computation time in hyperspectral image classification. In addition, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon. Consequently, traditional classification strategies have often limited performance in classification of hyperspectral imagery. Referring to the limitation of single classifiers in these situations, classifier ensemble system may exhibit better performance. This paper presents a method for classification of hyperspectral data based on two concepts of Band Clustering (BC) and Band Grouping [eg] through a Support Vector machine (SVM) ensemble system. The proposed method uses the BC\BG strategies to split data into few band portions. After this step, we applied SVM on each band cluster\group that is produced in previous step. Finally, Naive Bayes as a classifier fusion method combines the decisions of SVM classifiers. Experimental results show that the proposed method improves the classification accuracy in comparison to the standard SVM and to feature selection methods. Numéro de notice : A2013-362 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.6.523 En ligne : https://doi.org/10.14358/PERS.79.6.523 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32500
in Photogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 6 (June 2013) . - pp 523 - 533[article]STHist-C: a highly accurate cluster-based histogram for two and three dimensional geographic data points / Hai Thanh Mai in Geoinformatica, vol 17 n° 2 (April 2013)
[article]
Titre : STHist-C: a highly accurate cluster-based histogram for two and three dimensional geographic data points Type de document : Article/Communication Auteurs : Hai Thanh Mai, Auteur ; Jaeho Kim, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 325 - 352 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] base de données localisées
[Termes IGN] données localisées 2D
[Termes IGN] données localisées 3D
[Termes IGN] histogramme
[Termes IGN] regroupement de données
[Termes IGN] système d'information géographique
[Termes IGN] traitement de données localiséesRésumé : (Auteur) Histograms have been widely used for estimating selectivity in query optimization. In this paper, we propose a new histogram construction method for geographic data objects that are used in many real-world applications. The proposed method is based on analyses and utilization of clusters of objects that exist in a given data set, to build histograms with significantly enhanced accuracy. Our philosophy in allocating the histogram buckets is to allocate them to the subspaces that properly capture object clusters. Therefore, we first propose a procedure to find the centers of object clusters. Then, we propose an algorithm to construct the histogram buckets from these centers. The buckets are initialized from the clusters’ centers, then expanded to cover the clusters. Best expansion plans are chosen based on a notion of skewness gain. Results from extensive experiments using real-life data sets demonstrate that the proposed method can really improve the accuracy of the histograms further, when compared with the current state of the art histogram construction method for geographic data objects. Numéro de notice : A2013-162 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-012-0154-y Date de publication en ligne : 10/02/2012 En ligne : https://doi.org/10.1007/s10707-012-0154-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32300
in Geoinformatica > vol 17 n° 2 (April 2013) . - pp 325 - 352[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 057-2013021 RAB Revue Centre de documentation En réserve L003 Disponible Processing aggregated data: the location of clusters in health data / Kevin Buchin in Geoinformatica, vol 16 n° 3 (July 2012)
[article]
Titre : Processing aggregated data: the location of clusters in health data Type de document : Article/Communication Auteurs : Kevin Buchin, Auteur ; M. Buchin, Auteur ; Marc Van Kreveld, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 197 - 521 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] agrégation spatiale
[Termes IGN] base de données spatiotemporelles
[Termes IGN] base de données thématiques
[Termes IGN] géopositionnement
[Termes IGN] regroupement de données
[Termes IGN] santéRésumé : (Auteur) Spatially aggregated data is frequently used in geographical applications. Often spatial data analysis on aggregated data is performed in the same way as on exact data, which ignores the fact that we do not know the actual locations of the data. We here propose models and methods to take aggregation into account. For this we focus on the problem of locating clusters in aggregated data. More specifically, we study the problem of locating clusters in spatially aggregated health data. The data is given as a subdivision into regions with two values per region, the number of cases and the size of the population at risk. We formulate the problem as finding a placement of a cluster window of a given shape such that a cluster function depending on the population at risk and the cases is maximized. We propose area-based models to calculate the cases (and the population at risk) within a cluster window. These models are based on the areas of intersection of the cluster window with the regions of the subdivision. We show how to compute a subdivision such that within each cell of the subdivision the areas of intersection are simple functions. We evaluate experimentally how taking aggregation into account influences the location of the clusters found. Numéro de notice : A2012-108 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-011-0143-6 En ligne : https://doi.org/10.1007/s10707-011-0143-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31556
in Geoinformatica > vol 16 n° 3 (July 2012) . - pp 197 - 521[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 057-2012031 RAB Revue Centre de documentation En réserve L003 Disponible Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data / Ronald E. McRoberts in Remote sensing of environment, vol 115 n° 12 (december 2011)PermalinkAutomatic fuzzy clustering using modified differential evolution for image classification / U. Maulik in IEEE Transactions on geoscience and remote sensing, vol 48 n° 9 (September 2010)PermalinkUsing clustering methods in geospatial information systems / X. Wang in Geomatica, vol 64 n° 3 (September 2010)PermalinkSemantic-based pruning of redundant and uninteresting frequent geographic patterns / Vania Bogorny in Geoinformatica, vol 14 n° 2 (April 2010)PermalinkDelineation and geometric modeling of road networks / C. Poullis in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 2 (March - April 2010)PermalinkRegionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) / D. Guo in International journal of geographical information science IJGIS, vol 22 n° 6-7 (june 2008)PermalinkDesigning visual analytics methods for massive collections of movement data / Natalia Adrienko in Cartographica, vol 42 n° 2 (June 2007)PermalinkA new approach to the nearest-neighbour method to discover cluster features in overlaid spatial point processes / Tao Pei in International journal of geographical information science IJGIS, vol 20 n° 2 (february 2006)PermalinkClustérisation des calculs quotidiens du réseau GPS permanent, Volume 1. Rapport de stage / Yannick Carré (2005)PermalinkData-gathering strategies for social-behavioural research about participatory geographical information system use / T. Nyerges in International journal of geographical information science IJGIS, vol 16 n° 1 (january 2002)PermalinkEfficient polygon amalgamation methods for spatial OLAP and spatial data mining / X. Zhou (20/07/1999)PermalinkPermalinkRecognition of building clusters for generalization / Nicolas Regnauld (12/08/1996)Permalink