Letteratura scientifica selezionata sul tema "Spatial data mining"
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Articoli di riviste sul tema "Spatial data mining"
Wang, Shuliang, e Hanning Yuan. "Spatial Data Mining". International Journal of Data Warehousing and Mining 10, n. 4 (ottobre 2014): 50–70. http://dx.doi.org/10.4018/ijdwm.2014100103.
Testo completoWang, Ting. "Adaptive Tessellation Mapping (ATM) for Spatial Data Mining". International Journal of Machine Learning and Computing 4, n. 6 (2015): 478–82. http://dx.doi.org/10.7763/ijmlc.2014.v6.458.
Testo completoRastogi, Mohit. "Spatial data mining features between general data mining". South Asian Journal of Marketing & Management Research 11, n. 11 (2021): 96–101. http://dx.doi.org/10.5958/2249-877x.2021.00116.8.
Testo completoK, Sivakumar. "Spatial Data Mining: Recent Trends in the Era of Big Data". Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (25 luglio 2020): 912–16. http://dx.doi.org/10.5373/jardcs/v12sp7/20202182.
Testo completoMidoun, Mohammed, e Hafida Belbachir. "A new process for mining spatial databases: combining spatial data mining and visual data mining". International Journal of Business Information Systems 39, n. 1 (2022): 17. http://dx.doi.org/10.1504/ijbis.2022.120366.
Testo completoBelbachir, Hafida, e Mohammed Midoun. "A new process for mining spatial databases: combining spatial data mining and visual data mining". International Journal of Business Information Systems 1, n. 1 (2020): 1. http://dx.doi.org/10.1504/ijbis.2020.10024978.
Testo completoBist, Asmita, e Mainaz Faridi. "A Survey:On Spatial Data Mining". International Journal of Engineering Trends and Technology 46, n. 6 (25 aprile 2017): 327–33. http://dx.doi.org/10.14445/22315381/ijett-v46p257.
Testo completoFu, Chun Chang, e Nan Zhang. "The Application of Data Mining in GIS". Advanced Materials Research 267 (giugno 2011): 658–61. http://dx.doi.org/10.4028/www.scientific.net/amr.267.658.
Testo completoSanthosh Kumar, Ch N. "Spatial Data Mining using Cluster Analysis". International Journal of Computer Science and Information Technology 4, n. 4 (31 agosto 2012): 71–77. http://dx.doi.org/10.5121/ijcsit.2012.4407.
Testo completoWang, Shuliang, Deren Li, Wenzhong Shi, Deyi Li e Xinzhou Wang. "Cloud Model-Based Spatial Data Mining". Annals of GIS 9, n. 1-2 (dicembre 2003): 60–70. http://dx.doi.org/10.1080/10824000309480589.
Testo completoTesi sul tema "Spatial data mining"
Zhang, Xin Iris, e 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.
Testo completoYang, Zhao. "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data". ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2284.
Testo completoAl-Naymat, Ghazi. "NEW METHODS FOR MINING SEQUENTIAL AND TIME SERIES DATA". Thesis, The University of Sydney, 2009. http://hdl.handle.net/2123/5295.
Testo completoAl-Naymat, Ghazi. "NEW METHODS FOR MINING SEQUENTIAL AND TIME SERIES DATA". University of Sydney, 2009. http://hdl.handle.net/2123/5295.
Testo completoData 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 association rule problem, since there is no natural notion of transactions in spatial datasets that are embedded in continuous geographic space. Therefore, we have proposed an efficient algorithm (GridClique) to mine interesting spatial co-location patterns (maximal cliques). These patterns are used as the raw transactions for an association rule mining technique to discover complex co-location rules. Our proposal includes certain types of complex relationships – especially negative relationships – in the patterns. The relationships can be obtained from only the maximal clique patterns, which have never been used until now. Our approach is applied on a well-known astronomy dataset obtained from the Sloan Digital Sky Survey (SDSS). ST data is continuously collected and made accessible in the public domain. We present an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a predefined time. One approach to processing a “flock query” is to map ST data into high-dimensional space and to reduce the query to a sequence of standard range queries that can be answered using a spatial indexing structure; however, the performance of spatial indexing structures rapidly deteriorates in high-dimensional space. This thesis sets out a preprocessing strategy that uses a random projection to reduce the dimensionality of the transformed space. We use probabilistic arguments to prove the accuracy of the projection and to present experimental results that show the possibility of managing the curse of dimensionality in a ST setting by combining random projections with traditional data structures. In time series data mining, we devised a new space-efficient algorithm (SparseDTW) to compute the dynamic time warping (DTW) distance between two time series, which always yields the optimal result. This is in contrast to other approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series: the more the similarity between the time series, the less space required to compute the DTW between them. Other techniques for speeding up DTW, impose a priori constraints and do not exploit similarity characteristics that may be present in the data. Our experiments demonstrate that SparseDTW outperforms these approaches. We discover an interesting pattern by applying SparseDTW algorithm: “pairs trading” in a large stock-market dataset, of the index daily prices from the Australian stock exchange (ASX) from 1980 to 2002.
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.
Testo completoYang, 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.
Testo completoYu, Ping. "FP-tree Based Spatial Co-location Pattern Mining". Thesis, University of North Texas, 2005. https://digital.library.unt.edu/ark:/67531/metadc4724/.
Testo completoSHENCOTTAH, K. N. KALYANKUMAR. "FINDING CLUSTERS IN SPATIAL DATA". University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179521337.
Testo completoLin, Zhungshan. "Optimal Candidate Generation in Spatial Co-Location Mining". DigitalCommons@USU, 2009. https://digitalcommons.usu.edu/etd/377.
Testo completoPech, 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.
Testo completoWe propose a unique graph-based model to represent spatial data, non-spatial data and the spatial relations among spatial objects. We will generate datasets composed of graphs with a set of these three elements. We consider that by mining a dataset with these characteristics a graph-based mining tool can search patterns involving all these elements at the same time improving the results of the spatial analysis task. A significant characteristic of spatial data is that the attributes of the neighbors of an object may have an influence on the object itself. So, we propose to include in the model three relationship types (topological, orientation, and distance relations). In the model the spatial data (i. E. Spatial objects), non-spatial data (i. E. Non-spatial attributes), and spatial relations are represented as a collection of one or more directed graphs. A directed graph contains a collection of vertices and edges representing all these elements. Vertices represent either spatial objects, spatial relations between two spatial objects (binary relation), or non-spatial attributes describing the spatial objects. Edges represent a link between two vertices of any type. According to the type of vertices that an edge joins, it can represent either an attribute name or a spatial relation name. The attribute name can refer to a spatial object or a non-spatial entity. We use directed edges to represent directional information of relations among elements (i. E. Object x touches object y) and to describe attributes about objects (i. E. Object x has attribute z). We propose to adopt the Subdue system, a general graph-based data mining system developed at the University of Texas at Arlington, as our mining tool. A special feature named overlap has a primary role in the substructures discovery process and consequently a direct impact over the generated results. However, it is currently implemented in an orthodox way: all or nothing. Therefore, we propose a third approach: limited overlap, which gives the user the capability to set over which vertices the overlap will be allowed. We visualize directly three motivations issues to propose the implementation of the new algorithm: search space reduction, processing time reduction, and specialized overlapping pattern oriented search
Libri sul tema "Spatial data mining"
Li, Deren, Shuliang Wang e Deyi Li. Spatial Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48538-5.
Testo completoRoddick, John F., e Kathleen Hornsby, a cura di. Temporal, Spatial, and Spatio-Temporal Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45244-3.
Testo completoAlfred, Stein, Shi Whenzhong e Bijker Wietske 1965-, a cura di. Quality aspects in spatial data mining. Boca Raton, FL: Chapman & Hall/CRC, 2008.
Cerca il testo completoAlfred, Stein, Shi Wenzhong e Bijker Wietske 1965-, a cura di. Quality aspects in spatial data mining. Boca Raton, FL: Chapman & Hall/CRC, 2008.
Cerca il testo completoPourghasemi, Hamid Reza, e Mauro Rossi, a cura di. Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-73383-8.
Testo completoInternational Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining (2009 Wuhan, China). International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining: 13-14 October 2009, Wuhan, China. A cura di Liu Yaolin 1960-, Tang Xinming, Wuhan da xue. School of Resource and Environmental Science, China Jiao yu bu e SPIE (Society). Bellingham, Wash: SPIE, 2009.
Cerca il testo completoBu ting xie de si suo: Li Deren yuan shi wen ji. Wuchang: Wuhan da xue chu ban she, 2009.
Cerca il testo completo1974-, Wang Shuliang, e Li Deyi 1944-, a cura di. Kong jian shu ju wa jue li lun yu ying yong. Beijing: Ke xue chu ban she, 2006.
Cerca il testo completoBoris, Kovalerchuk, e Schwing James, a cura di. Visual and spatial analysis: Advances in data mining reasoning, and problem solving. Dordrecht: Springer, 2004.
Cerca il testo completoComputing with spatial trajectories. New York: Springer, c2011., 2011.
Cerca il testo completoCapitoli di libri sul tema "Spatial data mining"
Aggarwal, Charu C. "Mining Spatial Data". In Data Mining, 531–55. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8_16.
Testo completoLi, Deren, Shuliang Wang e Deyi Li. "GIS Data Mining". In Spatial Data Mining, 203–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48538-5_8.
Testo completoLi, Deren, Shuliang Wang e Deyi Li. "Data Field". In Spatial Data Mining, 175–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48538-5_6.
Testo completoShekhar, Shashi, Zhe Jiang, James Kang e Vijay Gandhi. "Spatial Data Mining". In Encyclopedia of Database Systems, 1–8. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_357-2.
Testo completoShekhar, Shashi, e Hui Xiong. "Spatial Data Mining". In Encyclopedia of GIS, 1087. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1257.
Testo completoShekhar, Shashi, James Kang e Vijay Gandhi. "Spatial Data Mining". In Encyclopedia of Database Systems, 2695–98. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_357.
Testo completoWang, Shuliang, e Tisinee Surapunt. "Spatial Data Mining". In Encyclopedia of Big Data Technologies, 1–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_66-1.
Testo completoShekhar, Shashi, Pusheng Zhang e Yan Huang. "Spatial Data Mining". In Data Mining and Knowledge Discovery Handbook, 837–54. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_43.
Testo completoWang, Shuliang, e Tisinee Surapunt. "Spatial Data Mining". In Encyclopedia of Big Data Technologies, 1546–55. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_66.
Testo completoShekhar, Shashi, Zhe Jiang, James Kang e Vijay Gandhi. "Spatial Data Mining". In Encyclopedia of Database Systems, 3575–83. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_357.
Testo completoAtti di convegni sul tema "Spatial data mining"
Bogorny, Vania, e Shashi Shekhar. "Spatial and Spatio-temporal Data Mining". In 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.166.
Testo completoMei, Kun, Yangge Tian e Fulin Bian. "Uncertainty in spatial data mining". In Second International Conference on Spatial Information Technology, a cura di Cheng Wang, Shan Zhong e Jiaolong Wei. SPIE, 2007. http://dx.doi.org/10.1117/12.775281.
Testo completoShuliang, Wang, Ding Gangyi e Zhong Ming. "Big spatial data mining". In 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691764.
Testo completoYang, Tie-li, Ping-Bai e Yu-Sheng Gong. "Spatial Data Mining Features between General Data Mining". In 2008 International Workshop on Geoscience and Remote Sensing (ETT and GRS). IEEE, 2008. http://dx.doi.org/10.1109/ettandgrs.2008.167.
Testo completoBinzani, Kanika, e Jin Soung Yoo. "Spark-based Spatial Association Mining". In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622419.
Testo completoLiu, Dianfeng, Yaolin Liu, Yin Xia, Xiaofeng Hong e Zhongjun Zhao. "Indicator mining model for spatial multi-scale degraded land evaluation". In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, a cura di Yaolin Liu e Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838297.
Testo completoWei, M., A. F. Sung e M. Cather. "Mining Spatially Abnormal Data in Spatial Databases". In Canadian International Petroleum Conference. Petroleum Society of Canada, 2004. http://dx.doi.org/10.2118/2004-142.
Testo completoSu, Hongjun, Yehua Sheng e Yongning Wen. "Data mining based on spectral and spatial features for hyperspectral classification". In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, a cura di Yaolin Liu e Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837304.
Testo completoZhang, Jie-lin. "Multisource geological data mining and its utilization of uranium resources exploration". In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, a cura di Yaolin Liu e Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837428.
Testo completoNiu, Jiqiang, Yaolin Liu, Feng Xu e Yang Zhang. "Data mining of synergetic coupling for land use based on extenics". In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, a cura di Yaolin Liu e Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837518.
Testo completoRapporti di organizzazioni sul tema "Spatial data mining"
Haeckel, Matthias, e Peter Linke. RV SONNE Fahrtbericht/Cruise Report SO268 - Assessing the Impacts of Nodule Mining on the Deep-sea Environment: NoduleMonitoring, Manzanillo (Mexico) – Vancouver (Canada), 17.02. – 27.05.2019. GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, novembre 2021. http://dx.doi.org/10.3289/geomar_rep_ns_59_20.
Testo completoGaffney, S., e P. Smyth. Final report: spatio-temporal data mining of scientific trajectory data. Office of Scientific and Technical Information (OSTI), gennaio 2001. http://dx.doi.org/10.2172/15005339.
Testo completoAnsari, S. M., E. M. Schetselaar e J. A. Craven. Three-dimensional magnetotelluric modelling of the Lalor volcanogenic massive-sulfide deposit, Manitoba. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/328003.
Testo completode Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.
Testo completoBowles, David, Michael Williams, Hope Dodd, Lloyd Morrison, Janice Hinsey, Tyler Cribbs, Gareth Rowell, Michael DeBacker, Jennifer Haack-Gaynor e Jeffrey Williams. Protocol for monitoring aquatic invertebrates of small streams in the Heartland Inventory & Monitoring Network: Version 2.1. National Park Service, aprile 2021. http://dx.doi.org/10.36967/nrr-2284622.
Testo completoNeyedley, K., J. J. Hanley, Z. Zajacz e M. Fayek. Accessory mineral thermobarometry, trace element chemistry, and stable O isotope systematics, Mooshla Intrusive Complex (MIC), Doyon-Bousquet-LaRonde mining camp, Abitibi greenstone belt, Québec. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328986.
Testo completoNeyedley, K., J. J. Hanley, P. Mercier-Langevin e M. Fayek. Ore mineralogy, pyrite chemistry, and S isotope systematics of magmatic-hydrothermal Au mineralization associated with the Mooshla Intrusive Complex (MIC), Doyon-Bousquet-LaRonde mining camp, Abitibi greenstone belt, Québec. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328985.
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