Relevant bibliographies by topics / Spatial association mining

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Spatial association mining'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Spatial association mining"

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Giovanni, Daian Rottoli, and Merlino Hernan. "Spatial association discovery process using frequent subgraph mining." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 1884–91. https://doi.org/10.12928/TELKOMNIKA.v18i4.13858.

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Spatial associations are one of the most relevant kinds of patterns used by business intelligence regarding spatial data. Due to the characteristics of this particular type of information, different approaches have been proposed for spatial association mining. This wide variety of methods has entailed the need for a process to integrate the activities for association discovery, one that is easy to implement and flexible enough to be adapted to any particular situation, particularly for small and medium-size projects to guide the useful pattern discovery process. Thus, this work proposes an adaptable knowledge discovery process that uses graph theory to model different spatial relationships from multiple scenarios, and frequent subgraph mining to discover spatial associations. A proof of concept is presented using real data.
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Shi, Wenzhong, Anshu Zhang, and Geoffrey I. Webb. "Mining significant crisp-fuzzy spatial association rules." International Journal of Geographical Information Science 32, no. 6 (2018): 1247–70. http://dx.doi.org/10.1080/13658816.2018.1434525.

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Lee, Anthony J. T., Ruey-Wen Hong, Wei-Min Ko, Wen-Kwang Tsao, and Hsiu-Hui Lin. "Mining spatial association rules in image databases." Information Sciences 177, no. 7 (2007): 1593–608. http://dx.doi.org/10.1016/j.ins.2006.09.018.

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Teegavarapu, Ramesh S. V. "Estimation of missing precipitation records integrating surface interpolation techniques and spatio-temporal association rules." Journal of Hydroinformatics 11, no. 2 (2009): 133–46. http://dx.doi.org/10.2166/hydro.2009.009.

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Deterministic and stochastic weighting methods are the most frequently used methods for estimating missing rainfall values. These methods may not always provide accurate estimates due to their inability to completely characterize the spatial and temporal variability of rainfall. A new association rule mining (ARM) based spatial interpolation approach is proposed, developed and investigated in the current study to estimate missing precipitation values at a gauging station. As an integrated approach this methodology combines the power of data mining techniques and spatial interpolation approaches. Data mining concepts are used to extract and formulate rules based on spatial and temporal associations among observed precipitation data series. The rules are then used to improve the precipitation estimates obtained from spatial interpolation methods. A stochastic spatial interpolation technique and three deterministic weighting methods are used as interpolation methods in the current study. Historical daily precipitation data obtained from 15 rain gauging stations from a temperate climatic region (Kentucky, USA) are used to test this approach and derive conclusions about its efficacy for estimating missing precipitation data. Results suggest that the use of association rule mining in conjunction with a spatial interpolation technique can improve the precipitation estimates.
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Tang, Xiao Bin, and Yi Zhi Zhang. "Research on Topology Association Rules Algorithm Based on Spatial Constraints." Advanced Materials Research 998-999 (July 2014): 915–20. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.915.

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Spatial topological relation is an important and typical multilayer spatial relation, when Apriori is used to mining spatial constraint topology association rules, it will has some repeated computing. And so this paper proposes an algorithm of spatial constraint topology association rules mining based on complement set, which is used to mining spatial multilayer transverse association rules with constraint condition from spatial database. This algorithm generates candidate frequent topological itemsets with constraint condition not only by down-top search strategy as Apriori, but also by computing complement set of candidate from down-top search strategy, which is suitable for mining any spatial topological frequent itemsets with constraint condition. This algorithm compresses a kind of spatial topological relation to form an integer. By the way, firstly, the algorithm may efficiently reduce some storage space when creating mining database. Secondly, the algorithm is fast to obtain topological relation between two spatial objects, namely, it may easily compute support of candidate frequent itemsets. Finally, the algorithm may fast generate candidate via double search strategy, i.e. one is that it connects (k+1)-candidate frequent itemsets with constraint condition of k-frequent itemsets as down-top search strategy, the other is that it computes complement set of (k+1)-candidate frequent itemsets with constraint condition. The result of experiment indicates that the algorithm is able to extract spatial multilayer transverse association rules with constraint condition from spatial database via efficient data store, and it is very efficient to extract any frequent topology association rules with constraint condition.
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Chen, J., S. Liu, P. Zhang, and Z. Sha. "A RESEARCH ON SPATIAL TOPOLOGICAL ASSOCIATION RULES MINING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B2 (July 25, 2012): 41–46. http://dx.doi.org/10.5194/isprsarchives-xxxix-b2-41-2012.

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Daián Rottoli, Giovanni, and Hernán Merlino. "Spatial association discovery process using frequent subgraph mining." TELKOMNIKA (Telecommunication Computing Electronics and Control) 18, no. 4 (2020): 1884. http://dx.doi.org/10.12928/telkomnika.v18i4.13858.

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Wei, Qian. "Product Shape Design Scheme Evaluation Method Based on Spatial Data Mining." Mathematical Problems in Engineering 2022 (July 20, 2022): 1–8. http://dx.doi.org/10.1155/2022/3231357.

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The stage of product modeling design implies a lot of complex tacit knowledge, which is the embodiment of the design concept centered on product modeling design and is also the hot spot and difficulty of modern design theory and method research. Aiming at the evaluation and decision of product modeling design scheme, a decision-making method of approaching ideal solution ranking based on grey relational analysis was proposed, which realized the convergence of tacit knowledge. The empty association rule is an important knowledge content of spatial data mining. A fuzzy genetic algorithm can solve the characteristics of random and nonlinear problems and solve the data mining problems of spatial association rules. The fuzzy genetic algorithm of discrete crossover probability and mutation probability is applied to data mining of spatial association rules in a spatial database, the coding method of the fuzzy genetic algorithm and the construction of fitness function are discussed, and the process of mining spatial association rules is given. The results show that the method of mining s association rules with the fuzzy genetic algorithm is feasible and has higher mining efficiency. This paper discusses the construction method of designing a decision support database based on linear regression and neural network and then proposes a decision method combining TOPSIS and grey relational analysis, which comprehensively considers the position and shape of the scheme data curve.
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Suci, Sri Utami Sutjipto, Sukaesih Sitanggang Imas, and Barus Baba. "Potential Usage Estimation of Ground Water using Spatial Association Rule Mining." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 1 (2017): 504–11. https://doi.org/10.12928/TELKOMNIKA.v15i1.4750.

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The utilization of ground water in the long term will lead to a number of negative impacts on groundwater resources and the environment, such as the decrease of groundwater level, seawater intrusion, land subsidence as well as scarcity of ground water. Furthermore, the use of ground water has directly affected the consumption pattern of Regional Water Company Bogor City (PDAM) customers. This study aims to determine the patterns and characteristics of PDAM customers in the utilization of ground water by using spatial association rule mining, so it can help PDAM to approximate the increase of customers that utilize ABT and the losses incurred. This research shows that as many as 53.362 (41.27%) PDAM customers that have the potential to use groundwater. The said customers are featured by several characteristics, such as being active customers, with monthly water bill of less than Rp. 53.358 and are not close to river.
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Mihai, Dana. "New approaches to processing GIS Data using Artificial Neural Networks models." Annals of the University of Craiova - Mathematics and Computer Science Series 48, no. 1 (2021): 358–73. http://dx.doi.org/10.52846/ami.v48i1.1551.

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Spatial data mining is a special type of data mining. The main difference between data mining and spatial data mining is that in spatial data mining tasks we use not only non-spatial attributes but also spatial attributes. Spatial data mining techniques have strong relationship with GIS (Geographical Information System) and are widely used in GIS for inferring association among spatial attributes, clustering and classifying information with respect to spatial attributes. In this paper we use the statistical package Weka on two models, which consist of two parcels plans from the Olt area of Romania. In our experimentation, we compare the results of the vector models depending on the values of the training datasets. Using these models with GIS data from the domain of Cadaster we analyze the performance of the Artificial Neural Networks in context of spatial data mining.
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Dissertations / Theses on the topic "Spatial association mining"

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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 associações do domínio geográfico, e são bem conhecidas, normalmente explicitamente representadas no esquema do banco de dados. A maioria dos algoritmos de mineração de regras de associação não garantem a eliminação de dependências geográficas conhecidas a priori. O resultado é que as mesmas associações representadas nos esquemas do banco de dados são extraídas pelos algoritmos de mineração de regras de associação e apresentadas ao usuário. O problema de mineração de regras de associação espacial pode ser dividido em três etapas principais: extração dos relacionamentos espaciais, geração dos conjuntos freqüentes e geração das regras de associação. A primeira etapa é a mais custosa tanto em tempo de processamento quanto pelo esforço requerido do usuário. A segunda e terceira etapas têm sido consideradas o maior problema na mineração de regras de associação em bancos de dados transacionais e tem sido abordadas como dois problemas diferentes: “frequent pattern mining” e “association rule mining”. Dependências geográficas bem conhecidas aparecem nas três etapas do processo. Tendo como objetivo a eliminação dessas dependências na mineração de regras de associação espacial essa tese apresenta um framework com três novos métodos para mineração de regras de associação utilizando restrições semânticas como conhecimento a priori. O primeiro método reduz os dados de entrada do algoritmo, e dependências geográficas são eliminadas parcialmente sem que haja perda de informação. O segundo método elimina combinações de pares de objetos geográficos com dependências durante a geração dos conjuntos freqüentes. O terceiro método é uma nova abordagem para gerar conjuntos freqüentes não redundantes e sem dependências, gerando conjuntos freqüentes máximos. Esse método reduz consideravelmente o número final de conjuntos freqüentes, e como conseqüência, reduz o número de regras de associação espacial.<br>The association rule mining technique emerged with the objective to find novel, useful, and previously unknown associations from transactional databases, and a large amount of association rule mining algorithms have been proposed in the last decade. Their main drawback, which is a well known problem, is the generation of large amounts of frequent patterns and association rules. In geographic databases the problem of mining spatial association rules increases significantly. Besides the large amount of generated patterns and rules, many patterns are well known geographic domain associations, normally explicitly represented in geographic database schemas. The majority of existing algorithms do not warrant the elimination of all well known geographic dependences. The result is that the same associations represented in geographic database schemas are extracted by spatial association rule mining algorithms and presented to the user. The problem of mining spatial association rules from geographic databases requires at least three main steps: compute spatial relationships, generate frequent patterns, and extract association rules. The first step is the most effort demanding and time consuming task in the rule mining process, but has received little attention in the literature. The second and third steps have been considered the main problem in transactional association rule mining and have been addressed as two different problems: frequent pattern mining and association rule mining. Well known geographic dependences which generate well known patterns may appear in the three main steps of the spatial association rule mining process. Aiming to eliminate well known dependences and generate more interesting patterns, this thesis presents a framework with three main methods for mining frequent geographic patterns using knowledge constraints. Semantic knowledge is used to avoid the generation of patterns that are previously known as non-interesting. The first method reduces the input problem, and all well known dependences that can be eliminated without loosing information are removed in data preprocessing. The second method eliminates combinations of pairs of geographic objects with dependences, during the frequent set generation. A third method presents a new approach to generate non-redundant frequent sets, the maximal generalized frequent sets without dependences. This method reduces the number of frequent patterns very significantly, and by consequence, the number of association rules.
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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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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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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 extraction of fuzzy association rules from it in order to improve decision-making about spatial data. This involves an extensive research about spatial knowledge discovery and how fuzzy logic can be used to develop it. It is stated that incorporating fuzzy logic to spatial data cube construction necessitates a new method for aggregation of fuzzy spatial data. We illustrate how this method also enhances the meaning of fuzzy spatial generalization rules and fuzzy association rules with a case-study about weather pattern searching. This study contributes to spatial knowledge discovery by generating more understandable and interesting knowledge from spatial data by extending spatial generalization with fuzzy memberships, extending the spatial aggregation in spatial data cube construction by utilizing weighted measures, and generating fuzzy association rules from the constructed fuzzy spatial data cube.
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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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Bookwala, Avinash Turab. "Combined map personalisation algorithm for delivering preferred spatial features in a map to everyday mobile device users." AUT University, 2009. http://hdl.handle.net/10292/920.

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In this thesis, we present an innovative and novel approach to personalise maps/geo-spatial services for mobile users. With the proposed map personalisation approach, only relevant data will be extracted from detailed maps/geo-spatial services on the fly, based on a user’s current location, preferences and requirements. This would result in dramatic improvements in the legibility of maps on mobile device screens, as well as significant reductions in the amount of data being transmitted; which, in turn, would reduce the download time and cost of transferring the required geo-spatial data across mobile networks. Furthermore, the proposed map personalisation approach has been implemented into a working system, based on a four-tier client server architecture, wherein fully detailed maps/services are stored on the server, and upon a user’s request personalised maps/services, extracted from the fully detailed maps/services based on the user’s current location, preferences, are sent to the user’s mobile device through mobile networks. By using open and standard system development tools, our system is open to everyday mobile devices rather than smart phones and Personal Digital Assistants (PDA) only, as is prevalent in most current map personalisation systems. The proposed map personalisation approach combines content-based information filtering and collaborative information filtering techniques into an algorithmic solution, wherein content-based information filtering is used for regular users having a user profile stored on the system, and collaborative information filtering is used for new/occasional users having no user profile stored on the system. Maps/geo-spatial services are personalised for regular users by analysing the user’s spatial feature preferences automatically collected and stored in their user profile from previous usages, whereas, map personalisation for new/occasional users is achieved through analysing the spatial feature preferences of like-minded users in the system in order to make an inference for the target user. Furthermore, with the use of association rule mining, an advanced inference technique, the spatial features retrieved for new/occasional users through collaborative filtering can be attained. The selection of spatial features through association rule mining is achieved by finding interesting and similar patterns in the spatial features most commonly retrieved by different user groups, based on their past transactions or usage sessions with the system.
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Unal, Calargun Seda. "Fuzzy Association Rule Mining From Spatio-temporal Data: An Analysis Of Meteorological Data In Turkey." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609308/index.pdf.

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Data mining is the extraction of interesting non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases. Association rule mining is a data mining method that seeks to discover associations among transactions encoded within a database. Data mining on spatio-temporal data takes into consideration the dynamics of spatially extended systems for which large amounts of spatial data exist, given that all real world spatial data exists in some temporal context. We need fuzzy sets in mining association rules from spatio-temporal databases since fuzzy sets handle the numerical data better by softening the sharp boundaries of data which models the uncertainty embedded in the meaning of data. In this thesis, fuzzy association rule mining is performed on spatio-temporal data using data cubes and Apriori algorithm. A methodology is developed for fuzzy spatio-temporal data cube construction. Besides the performance criteria interpretability, precision, utility, novelty, direct-to-the-point and visualization are defined to be the metrics for the comparison of association rule mining techniques. Fuzzy association rule mining using spatio-temporal data cubes and Apriori algorithm performed within the scope of this thesis are compared using these metrics. Real meteorological data (precipitation and temperature) for Turkey recorded between 1970 and 2007 are analyzed using data cube and Apriori algorithm in order to generate the fuzzy association rules.
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Kinuthia, Wanyee. "“Accumulation by Dispossession” by the Global Extractive Industry: The Case of Canada." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/30170.

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This thesis draws on David Harvey’s concept of “accumulation by dispossession” and an international political economy (IPE) approach centred on the institutional arrangements and power structures that privilege certain actors and values, in order to critique current capitalist practices of primitive accumulation by the global corporate extractive industry. The thesis examines how accumulation by dispossession by the global extractive industry is facilitated by the “free entry” or “free mining” principle. It does so by focusing on Canada as a leader in the global extractive industry and the spread of this country’s mining laws to other countries – in other words, the transnationalisation of norms in the global extractive industry – so as to maintain a consistent and familiar operating environment for Canadian extractive companies. The transnationalisation of norms is further promoted by key international institutions such as the World Bank, which is also the world’s largest development lender and also plays a key role in shaping the regulations that govern natural resource extraction. The thesis briefly investigates some Canadian examples of resource extraction projects, in order to demonstrate the weaknesses of Canadian mining laws, particularly the lack of protection of landowners’ rights under the free entry system and the subsequent need for “free, prior and informed consent” (FPIC). The thesis also considers some of the challenges to the adoption and implementation of the right to FPIC. These challenges include embedded institutional structures like the free entry mining system, international political economy (IPE) as shaped by international institutions and powerful corporations, as well as concerns regarding ‘local’ power structures or the legitimacy of representatives of communities affected by extractive projects. The thesis concludes that in order for Canada to be truly recognized as a leader in the global extractive industry, it must establish legal norms domestically to ensure that Canadian mining companies and residents can be held accountable when there is evidence of environmental and/or human rights violations associated with the activities of Canadian mining companies abroad. The thesis also concludes that Canada needs to address underlying structural issues such as the free entry mining system and implement FPIC, in order to curb “accumulation by dispossession” by the extractive industry, both domestically and abroad.
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Yi-Ling, Chen. "Mining Spatial Association Rules in Image." 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-0907200516580400.

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Chen, Yi-Ling, and 陳奕伶. "Mining Spatial Association Rules in Image." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/39410529091384817730.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>93<br>In this paper, we integrate data mining with image processing for discovering spatial relationships in images. We present an image mining framework, Spatial Association Rulemining (SAR), to mine spatial associations located in specific locations of images. A rule in the SAR refers to the occurrences of image content in a pair of spatial locations. The proposed approach is applied to mine color spatial association rules (color-SAR) in landscape scene images so as to demonstrate that the spatial association rules is able to the application of image classification. Our experimental results show that the classification accuracy of 86% can be achieved by the rule-based classifier.
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Book chapters on the topic "Spatial association mining"

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Shekhar, Shashi, and Hui Xiong. "Mining Spatial Association Patterns." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_788.

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Bembenik, Robert, and Grzegorz Protaziuk. "Mining Spatial Association Rules." In Intelligent Information Processing and Web Mining. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39985-8_1.

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Han, Jiawei, Anthony K. H. Tung, and Jing He. "SPARC: Spatial Association Rule-Based Classification." In Data Mining for Scientific and Engineering Applications. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1733-7_25.

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Qian, Min, Li-Jie Pu, Rong Fu, and Ming Zhu. "Mining Spatial Association Rules with Multi-relational Approach." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17316-5_28.

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Isik, Narin, and Adnan Yazici. "Association Rule Mining using Fuzzy Spatial Data Cubes." In Geographic Uncertainty in Environmental Security. Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-6438-8_12.

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Ceci, Michelangelo, Corrado Loglisci, Eliana Salvemini, Domenica D’Elia, and Donato Malerba. "Mining Spatial Association Rules for Composite Motif Discovery." In Mathematical Approaches to Polymer Sequence Analysis and Related Problems. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6800-5_5.

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van Hemert, Jano, and Richard Baldock. "Mining Spatial Gene Expression Data for Association Rules." In Bioinformatics Research and Development. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71233-6_6.

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Ding, Wei, and Christoph F. Eick. "Regional Association Rule Mining and Scoping from Spatial Data." In Intelligent Systems Reference Library. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23166-7_11.

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Yoo, Jin Soung, Wentao Shao, and Kanika Binzani. "Spatial Association Pattern Mining Using In-Memory Computational Framework." In Big Data – BigData 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59612-5_17.

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Saritha, S., and G. SanthoshKumar. "Semantic Association Mining on Spatial Patterns in Medical Images." In Advances in Computing and Communications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22714-1_28.

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Conference papers on the topic "Spatial association mining"

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Liu, Weitong, Zhicheng Wen, Xiaoyu Li, and Ping Wang. "Association Rule Mining in Urban Spatial Data Mining in Digital Twin Modeling." In 2024 International Conference on Interactive Intelligent Systems and Techniques (IIST). IEEE, 2024. http://dx.doi.org/10.1109/iist62526.2024.00005.

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Garces, Daniel, Yolanda Sanchez-Palencia, Samantha Jimenez, and Juan Llamas. "ENVIRONMENTAL STATISTICAL ANALYSIS OF THE BASINS OF THE MINING DISTRICT OF PONCE ENRIQUEZ (ECUADOR)." In 24th SGEM International Multidisciplinary Scientific GeoConference 24. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/1.1/s01.09.

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Camilo Ponce Enriquez is in the southwest coastal zone of Ecuador, and it is surrounded by 4 river basins (Gala, Tenguel, Siete and Pagua) with an area of 1,780 km2. Mining has been the main economic activity since 1980 and is classified as Artisanal and Small-Scale Mining Activity. However, since 2000s there is evidence of contamination by heavy metals in water and sediments due to irregular mining practices and inefficiency environmental�s regulations. Despite the evidence, there are not enough scientific studies, and few reports are published about chemistry and contamination. The study goal is to analyze 333 river sediments samples taken from the four river basins, using geostatistical and geochemical methods, to determinate possible correlations and associations between contaminant elements. Samples were analyzed for 35 elements (Ag, Al, As, Au, Ba, Bi, Ca, Cd, Co, Cr, Cu, Fe, Ga, Hg, K, La, Li, Mg, Mn, Mo, Na, Nb, Ni, Pb, Sb, Sc, Sr, Te, Ti, Tl, V, W, Y, Zn, Zr). The elements results were studied using univariate statistical methods, Spearman's Ordinal Correlation, factor analysis using PCA, and cluster analysis using Ward�s method. The results evidence nine elements (Co, Cr, Cu, Fe, Mg, Ni, Sc, Ti and V) with significant correlations (r ? 0.5). The strongest correlation is V-Fe (0.91), followed by V-Ti, Ni-Co and Ni-Cr and Sc-Co (r ? 0.8). The correlation in the V, Fe, Ni, Cr elements are related with orthomagmatic deposits. Also, V and Ti are related to titanium and iron oxides mainly in mafic deposits, which could be associated with the Pallatanga Fm. The factor analysis identified five groups of elements that are comparable to the Goldschmidt classification, where the groups are related with arsenopyrite, oxy-hydroxides, mafic and porphyry deposits. The cluster analysis revealed four groups, where the spatial distribution matched with the contamination level areas in the Siete, Tenguel and Gala River basins. The results provide an environmental baseline against which the impacts of anthropogenic activities (including mining) may be assessed. A basis for the formulation of sediment quality criteria and for the identification of natural geochemical hazards is also provided.
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Binzani, Kanika, and 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.

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Li, Xiaolei. "Mining spatial association rules in spatially heterogeneous environment." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815757.

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Yoo, Jin Soung, and Douglas Boulware. "Incremental and parallel spatial association mining." In 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004499.

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Chen, Jiangping, Yanan Chen, Jie Yu, and Zhaohui Yang. "Comparisons with spatial autocorrelation and spatial association rule mining." In 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM). IEEE, 2011. http://dx.doi.org/10.1109/icsdm.2011.5969000.

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Park, Sang Jun, and Jin Soung Yoo. "Leveraging cloud computing for spatial association mining." In 2014 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2014. http://dx.doi.org/10.1109/smc.2014.6974590.

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Sha, Zongyao, and Xiaolei Li. "Mining local association patterns from spatial dataset." In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2010. http://dx.doi.org/10.1109/fskd.2010.5569205.

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Zhao, Xiaohui, and Yu Fang. "A Grid-based Spatial Association Mining Method." In Sixth International Conference on Grid and Cooperative Computing (GCC 2007). IEEE, 2007. http://dx.doi.org/10.1109/gcc.2007.12.

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Zhu, Hongmei, and Yu Luo. "Analysis of obstruction reason of urban sewer using spatial association rules." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838538.

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Reports on the topic "Spatial association mining"

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Neyedley, K., J. J. Hanley, P. Mercier-Langevin, and 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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Abstract:
The Mooshla Intrusive Complex (MIC) is an Archean polyphase magmatic body located in the Doyon-Bousquet-LaRonde (DBL) mining camp of the Abitibi greenstone belt, Québec. The MIC is spatially associated with numerous gold (Au)-rich VMS, epizonal 'intrusion-related' Au-Cu vein systems, and shear zone-hosted (orogenic?) Au deposits. To elucidate genetic links between deposits and the MIC, mineralized samples from two of the epizonal 'intrusion-related' Au-Cu vein systems (Doyon and Grand Duc Au-Cu) have been characterized using a variety of analytical techniques. Preliminary results indicate gold (as electrum) from both deposits occurs relatively late in the systems as it is primarily observed along fractures in pyrite and gangue minerals. At Grand Duc gold appears to have formed syn- to post-crystallization relative to base metal sulphides (e.g. chalcopyrite, sphalerite, pyrrhotite), whereas base metal sulphides at Doyon are relatively rare. The accessory ore mineral assemblage at Doyon is relatively simple compared to Grand Duc, consisting of petzite (Ag3AuTe2), calaverite (AuTe2), and hessite (Ag2Te), while accessory ore minerals at Grand Duc are comprised of tellurobismuthite (Bi2Te3), volynskite (AgBiTe2), native Te, tsumoite (BiTe) or tetradymite (Bi2Te2S), altaite (PbTe), petzite, calaverite, and hessite. Pyrite trace element distribution maps from representative pyrite grains from Doyon and Grand Duc were collected and confirm petrographic observations that Au occurs relatively late. Pyrite from Doyon appears to have been initially trace-element poor, then became enriched in As, followed by the ore metal stage consisting of Au-Ag-Te-Bi-Pb-Cu enrichment and lastly a Co-Ni-Se(?) stage enrichment. Grand Duc pyrite is more complex with initial enrichments in Co-Se-As (Stage 1) followed by an increase in As-Co(?) concentrations (Stage 2). The ore metal stage (Stage 3) is indicated by another increase in As coupled with Au-Ag-Bi-Te-Sb-Pb-Ni-Cu-Zn-Sn-Cd-In enrichment. The final stage of pyrite growth (Stage 4) is represented by the same element assemblage as Stage 3 but at lower concentrations. Preliminary sulphur isotope data from Grand Duc indicates pyrite, pyrrhotite, and chalcopyrite all have similar delta-34S values (~1.5 � 1 permille) with no core-to-rim variations. Pyrite from Doyon has slightly higher delta-34S values (~2.5 � 1 permille) compared to Grand Duc but similarly does not show much core-to-rim variation. At Grand Duc, the occurrence of Au concentrating along the rim of pyrite grains and associated with an enrichment in As and other metals (Sb-Ag-Bi-Te) shares similarities with porphyry and epithermal deposits, and the overall metal association of Au with Te and Bi is a hallmark of other intrusion-related gold systems. The occurrence of the ore metal-rich rims on pyrite from Grand Duc could be related to fluid boiling which results in the destabilization of gold-bearing aqueous complexes. Pyrite from Doyon does not show this inferred boiling texture but shares characteristics of dissolution-reprecipitation processes, where metals in the pyrite lattice are dissolved and then reconcentrated into discrete mineral phases that commonly precipitate in voids and fractures created during pyrite dissolution.
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