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Journal articles on the topic 'Spatial association mining'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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1

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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7

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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Liu, Yaolin, Peng Xie, Qingsong He, Xiang Zhao, Xiaojian Wei, and Ronghui Tan. "A new method based on association rules mining and geo-filter for mining spatial association knowledge." Chinese Geographical Science 27, no. 3 (2017): 389–401. http://dx.doi.org/10.1007/s11769-017-0873-y.

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12

Guo, Yan, Xiaonan Hu, Zepeng Wang, et al. "The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining." Agricultural Economics (Zemědělská ekonomika) 67, No. 11 (2021): 457–67. http://dx.doi.org/10.17221/128/2021-agricecon.

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With the advent of the era of big data, data mining methods show their powerful information mining ability in various fields, seeking the association information hidden in the data, which is convenient for people to make scientific decisions. This paper analyses the butterfly effect in the agricultural product industry chain from the perspective of producer and consumer by using multidimensional time and space theory and proposes a new price forecasting method. We consider that the price change of agricultural products is not only affected by the balance of market supply and demand but also by the factors of time and space. Taking the pig industry chain of Sichuan Province as an example, this paper explores and excavates the data from 2010 to 2020 in the time dimension. Interestingly, we found that the price changes in pork in the market are generally highly correlated with the prices of slaughtered pigs, piglets a few weeks ago and the prices of multiple feed a few months ago. Based on the precise time-space factors, we improved the price forecasting model, greatly improved the accuracy of price prediction, and proved the effectiveness of multidimensional spatiotemporal association mining. The research in this paper is helpful to establish a brand-new agricultural product price prediction theory, which is of great significance to the development of the agricultural economy and global poverty alleviation.

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Vyas, Ranjana, Lokesh Kumar Sharma, and U. S. Tiwary. "Exploring Spatial ARM (Spatial Association Rule Mining) for Geo-Decision Support System." Journal of Computer Science 3, no. 11 (2007): 882–86. http://dx.doi.org/10.3844/jcssp.2007.882.886.

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14

Dao, Thi Hong Diep, and Jean-Claude Thill. "The SpatialARMED Framework: Handling Complex Spatial Components in Spatial Association Rule Mining." Geographical Analysis 48, no. 3 (2016): 248–74. http://dx.doi.org/10.1111/gean.12094.

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15

Niu, X., and X. Ji. "Evaluation methods for association rules in spatial knowlegde base." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4 (April 23, 2014): 53–58. http://dx.doi.org/10.5194/isprsannals-ii-4-53-2014.

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Association rule is an important model in data mining. It describes the relationship between predicates in transactions, makes the expression of knowledge hidden in data more specific and clear. While the developing and applying of remote sensing technology and automatic data collection tools in recent decades, tremendous amounts of spatial and non-spatial data have been collected and stored in large spatial database, so association rules mining from spatial database becomes a significant research area with extensive applications. How to find effective, reliable and interesting association rules from vast information for helping people analyze and make decision has become a significant issue. Evaluation methods measure spatial association rules with evaluation criteria. On the basis of analyzing the existing evaluation criteria, this paper improved the novelty evaluation method, built a spatial knowledge base, and proposed a new evaluation process based on the support-confidence evaluation system. Finally, the feasibility of the new evaluation process was validated by an experiment with real-world geographical spatial data.

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Sukaesih Sitanggang, Imas. "Spatial Multidimensional Association Rules Mining in Forest Fire Data." Journal of Data Analysis and Information Processing 01, no. 04 (2013): 90–96. http://dx.doi.org/10.4236/jdaip.2013.14010.

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17

Chen Shougang. "Efficient Spatial Association Rule Mining Algorithm based on Region." International Journal of Advancements in Computing Technology 4, no. 23 (2012): 211–18. http://dx.doi.org/10.4156/ijact.vol4.issue23.25.

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18

Jia, Lianyin, Haotian Tang, Mengjuan Li, Bingxin Zhao, Shoulin Wei, and Haihe Zhou. "An Efficient Association Rule Mining-Based Spatial Keyword Index." International Journal of Data Warehousing and Mining 19, no. 2 (2023): 1–19. http://dx.doi.org/10.4018/ijdwm.316161.

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Spatial keyword query has attracted the attention of many researchers. Most of the existing spatial keyword indexes do not consider the differences in keyword distribution, so their efficiencies are not high when data are skewed. To this end, this paper proposes a novel association rule mining based spatial keyword index, ARM-SQ, whose inverted lists are materialized by the frequent item sets mined by association rules; thus, intersections of long lists can be avoided. To prevent excessive space costs caused by materialization, a depth-based materialization strategy is introduced, which maintains a good balance between query and space costs. To select the right frequent item sets for answering a query, the authors further implement a benefit-based greedy frequent item set selection algorithm, BGF-Selection. The experimental results show that this algorithm significantly outperforms the existing algorithms, and its efficiency can be an order of magnitude higher than SFC-Quad.

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19

Shu, Si Hui, and Zi Zhi Lin. "Algorithms of Mining Maximum Frequent Itemsets Based on Compression Matrix." Applied Mechanics and Materials 571-572 (June 2014): 57–62. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.57.

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Association rule mining is one of the most important and well researched techniques of data mining, the key procedure of the association rule mining is to find frequent itemsets , the frequent itemsets are easily obtained by maximum frequent itemsets. so finding maximum frequent itemsets is one of the most important strategies of association data mining. Algorithms of mining maximum frequent itemsets based on compression matrix are introduced in this paper. It mainly obtains all maximum frequent itemsets by simply removing a set of rows and columns of transaction matrix, which is easily programmed recursive algorithm. The new algorithm optimizes the known association rule mining algorithms based on matrix given by some researchers in recent years, which greatly reduces the temporal complexity and spatial complexity, and highly promotes the efficiency of association rule mining.

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Lin, Zi Zhi, Si Hui Shu, and Yun Ding. "Algorithm of Mining Association Rule Based on Matrix." Applied Mechanics and Materials 513-517 (February 2014): 786–91. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.786.

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Association rule mining is one of the most important techniques of data mining. Algorithms based on matrix are efficient due to only scanning the transaction database for one time. In this paper, an algorithm of association rule mining based on the compression matrix is given. It mainly compresses the transaction matrix by integrating various strategies and fleetly finds frequent itemsets. The new algorithm optimizes the known algorithms of mining association rule based on matrix given by some researchers in recent years, which greatly reduces the temporal and spatial complexity, and highly promotes the efficiency of finding frequent itemsets.

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Petry, Frederick E. "Data Mining Approaches for Geo-Spatial Big Data." International Journal of Organizational and Collective Intelligence 3, no. 1 (2012): 52–71. http://dx.doi.org/10.4018/joci.2012010104.

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The availability of a vast amount of heterogeneous information from a variety of sources ranging from satellite imagery to the Internet has been termed as the problem of Big Data. Currently there is a great emphasis on the huge amount of geophysical data that has a spatial basis or spatial aspects. To effectively utilize such volumes of data, data mining techniques are needed to manage discovery from such volumes of data. An important consideration for this sort of data mining is to extend techniques to manage the inherent uncertainty involved in such spatial data. In this paper the authors first provide overviews of uncertainty representations based on fuzzy, intuitionistic, and rough sets theory and data mining techniques. To illustrate the issues they focus on the application of the discovery of association rules in approaches for vague spatial data. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets are described. Finally an example of rule extraction for both fuzzy and rough set types of uncertainty representations is given

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Ladner, R., F. E. Petry, and M. A. Cobb. "Fuzzy Set Approaches to Spatial Data Mining of Association Rules." Transactions in GIS 7, no. 1 (2003): 123–38. http://dx.doi.org/10.1111/1467-9671.00133.

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23

Wang, Qiang, and Vasileios Megalooikonomou. "A performance evaluation framework for association mining in spatial data." Journal of Intelligent Information Systems 35, no. 3 (2009): 465–94. http://dx.doi.org/10.1007/s10844-009-0115-6.

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Arunadevi, J., and Dr V. Rajamani. "Optimization of Spatial Association Rule Mining using Hybrid Evolutionary Algorithm." International Journal of Computer Applications 1, no. 19 (2010): 91–94. http://dx.doi.org/10.5120/397-592.

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Ma, Jun, and Dong Dong Zhang. "Application of the Parallel Spatial Association Rule in Remote Sensing Data Mining." Key Engineering Materials 500 (January 2012): 598–602. http://dx.doi.org/10.4028/www.scientific.net/kem.500.598.

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Since the remote sensing data are multi-resources and massive, the common data mining algorithm cannot effectively discover the knowledge what people want to know. However, spatial association rule can solve the problem of inefficiency in remote sensing data mining. This paper gives an algorithm to compute the frequent item sets though a method like calculating vectors inner-product. And the algorithm will introduce pruning in the whole running. It reduces the time and resources consumption effectively

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Lin, Zi Zhi, and Si Hui Shu. "Mining Frequent Itemsets Algorithm Based on Compression Matrix." Applied Mechanics and Materials 556-562 (May 2014): 3501–5. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3501.

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Association rule mining is one of the most important and well researched techniques of data mining. The key procedure of the association rule mining is to find frequent itemsets. In this paper, a new mining frequent itemsets algorithm based on matrix is introduced. Frequent itemsets are obtained by compressing the transaction matrix efficiently by a new strategy. The new algorithm optimizes the known mining frequent itemsets algorithms based on matrix given by some researchers in recent years, which greatly reduces the temporal complexity and spatial complexity. It is more feasible especially when the degrees of the frequent itemsets are high.

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Al-Heety, Emad A. M. "SPATIAL ANALYSIS OF EARTHQUAKES IN IRAQ USING STATISTICAL AND DATA MINING TECHNIQUES." Iraqi Geological Journal 39-49, no. 2 (2016): 1–15. http://dx.doi.org/10.46717/igj.39-49.2.1ms-2016-12-24.

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Statistical and data mining techniques (DMTs) are applied to an earthquakes catalogue of Iraq to study the spatial distribution pattern of earthquakes over the period from 1900 to 2010. The employed techniques are Quadrant Account Analysis (QCA), Tree-clustering, k-means Clustering, Association rules, and Linear Regression. Results of QCA showed that the pattern of earthquake occurrence beneath Iraq was spatially clustered. According to results of application of tree-clustering, earthquakes were grouped into nine clusters depending on degree of similarity between events. Results K-means clustering confirmed results of tree-clustering. Application of association rules failed to generate association rules between the earthquakes parameters (location, depth and magnitude, ...etc.). A weak relationship between depth and magnitude was the result of application of linear regression.

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Parvathi, R., and S. Palaniammal. "A Novel Hybrid Spatial Association Rule Mining Algorithm for Neuro Imaging." International Journal of Computer Applications 8, no. 9 (2010): 32–37. http://dx.doi.org/10.5120/1233-1616.

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CHEN, Jiangping, and Bingjian HUANG. "Application and Effects of Data Spatial Autocorrelation on Association Rule Mining." Geo-information Science 13, no. 1 (2011): 109–17. http://dx.doi.org/10.3724/sp.j.1047.2011.00109.

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Sutjipto, Suci Sri Utami, Imas Sukaesih Sitanggang, and Baba Barus. "Potential Usage Estimation of Ground Water using Spatial Association Rule Mining." TELKOMNIKA (Telecommunication Computing Electronics and Control) 15, no. 1 (2017): 504. http://dx.doi.org/10.12928/telkomnika.v15i1.4750.

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Ma, Weixuan, Cunjin Xue, and Junqi Zhou. "Mining time-series association rules from Western Pacific spatial-temporal data." IOP Conference Series: Earth and Environmental Science 17 (March 18, 2014): 012224. http://dx.doi.org/10.1088/1755-1315/17/1/012224.

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32

Cai, Fei, Jie Chen, Telin Chen, Banghua Zhang, and Wenping Fan. "Mining significant local spatial association rules for multi-category point data." Heliyon 10, no. 3 (2024): e25047. http://dx.doi.org/10.1016/j.heliyon.2024.e25047.

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Jang, Hong-Jun, Yeongwook Yang, Ji Su Park, and Byoungwook Kim. "FP-Growth Algorithm for Discovering Region-Based Association Rule in the IoT Environment." Electronics 10, no. 24 (2021): 3091. http://dx.doi.org/10.3390/electronics10243091.

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With the development of the Internet of things (IoT), both types and amounts of spatial data collected from heterogeneous IoT devices are increasing. The increased spatial data are being actively utilized in the data mining field. The existing association rule mining algorithms find all items with high correlation in the entire data. Association rules that may appear differently for each region, however, may not be found when the association rules are searched for all data. In this paper, we propose region-based frequent pattern growth (RFP-Growth) to search for association rules by dense regions. First, RFP-Growth divides item transaction included position data into regions by a density-based clustering algorithm. Second, frequent pattern growth (FP-Growth) is performed for each transaction divided by region. The experimental results show that RFP-Growth discovers new association rules that the original FP-Growth cannot find in the whole data.

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Ding, Zhi, Xiaohan Liao, Fenzhen Su, and Dongjie Fu. "Mining Coastal Land Use Sequential Pattern and Its Land Use Associations Based on Association Rule Mining." Remote Sensing 9, no. 2 (2017): 116. http://dx.doi.org/10.3390/rs9020116.

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Abstract: Research on the land use of the coastal zone in the sea–land direction will not only reveal its land use distribution, but may also indicate the interactions between inland land use and the ocean through associations between inland land use and seaward land use indirectly. However, in the existing research, few have paid attention to the land use in sea–land direction, let alone the sequential relationship between land-use types. The sequential relationship would be useful in land use planning and rehabilitation of the landscape in the sea–land direction, and the association between land-use types, particularly the inland land use and seaward land use, is not discussed. Therefore, This study presents a model named ARCLUSSM (Association Rules-based Coastal Land use Spatial Sequence Model) to mine the sequential pattern of land use with interesting associations in the sea–land direction of the coastal zone. As a case study, the typical coastal zone of Bohai Bay and the Yellow River delta in China was used. The results are as follows: firstly, 27 interesting association patterns of land use in the sea–land direction of the coastal zone were mined easily. Both sequential relationship and distance between land-use types for 27 patterns among six land-use types were mined definitely, and the sequence of the six land-use types tended to be tidal flat > shrimp pond > reservoir/artificial pond > settlement > river > dry land in sea–land direction. These patterns would offer specific support for land-use planning and rehabilitation of the coastal zone. There were 19 association patterns between seaward and landward land-use types. These patterns showed strong associations between seaward and landward land-use types. It indicated that the landward land use might have some impacts on the seaward land use, or in the other direction, which may help to reveal the interactions between inland land use and the ocean. Thus, the ARCLUSSM was an efficient tool to mine the sequential relationship and distance between land-use types with interesting association rules in the sea–land direction, which would offer practicable advice to appropriate coastal zone management and planning, and might reveal the interactions between inland land use and the ocean.

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R., Rajamani*1 &. S. Saranya2. "A STUDY OF TEXT MINING METHODS, APPLICATIONS,AND TECHNIQUES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 623–28. https://doi.org/10.5281/zenodo.829803.

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Data mining is used to extract useful information from the large amount of data. It is used to implement and solve different types of research problems. The research related areas in data mining are text mining, web mining, image mining, sequential pattern mining, spatial mining, medical mining, multimedia mining, structure mining and graph mining. Text mining also referred to text of data mining, it is also called knowledge discovery in text (KDT) or knowledge of intelligent text analysis. The process is driving high-quality information from not-structured to semi-structured data. Text mining is the discovery by automatically extracting information from different written resources and also by computer for extracting new, previously unknown information. This paper discusses about the process of text mining, methods, tools, applications and techniques.

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Höpken, Wolfram, Marcel Müller, Matthias Fuchs, and Maria Lexhagen. "Flickr data for analysing tourists’ spatial behaviour and movement patterns." Journal of Hospitality and Tourism Technology 11, no. 1 (2020): 69–82. http://dx.doi.org/10.1108/jhtt-08-2017-0059.

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Purpose The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios. Design/methodology/approach The study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns. Findings The approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent). Research limitations/implications As a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour. Practical implications From a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment. Originality/value The study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios.

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Wu, Jie. "Evaluation Model of Product Shape Design Scheme Based on Fuzzy Genetic Algorithm Mining Spatial Association Rules." Mathematical Problems in Engineering 2022 (March 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/2888472.

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Put forward a kind of association rules mining method based on fuzzy genetic algorithm, this approach by building a mining model, the association rules and fuzzy genetic algorithm fuses in together, and then given to the fitness function of the mining space, and uses threshold to limit the fuzzy genetic algorithm will cross distribution and compile the fitness function, the improved method excavation stability is strong, the mining accuracy is high. The clustering analysis method of multidimensional fuzzy genetic algorithm mapping association network is studied, and the multidimensional module layout target is analyzed by using fuzzy hierarchical analysis technology and improved genetic algorithm combined with the clustering target of each angle, and the module division of each angle is realized. The main structure of the product is constructed with process model as the integration framework, style as the organization form, and feature list as the expression mechanism. The product characteristics based on fuzzy genetic algorithm are studied, the main structure configuration design process model mapping relation analysis, combined with the main structure of the joint model, together to achieve the fuzzy genetic algorithm (GA) variant design of fine-grained axiomatic mode, based on the associated network building and integration of new product design process, product structure of multidimensional optimization problem is solved.

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Clementini, Eliseo, Paolino Di Felice, and Krzysztof Koperski. "Mining multiple-level spatial association rules for objects with a broad boundary." Data & Knowledge Engineering 34, no. 3 (2000): 251–70. http://dx.doi.org/10.1016/s0169-023x(00)00017-3.

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Xue, C. J., Q. Dong, and W. X. Ma. "Object-oriented spatial-temporal association rules mining on ocean remote sensing imagery." IOP Conference Series: Earth and Environmental Science 17 (March 18, 2014): 012109. http://dx.doi.org/10.1088/1755-1315/17/1/012109.

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Ding, Wei, Christoph F. Eick, Xiaojing Yuan, Jing Wang, and Jean-Philippe Nicot. "A framework for regional association rule mining and scoping in spatial datasets." GeoInformatica 15, no. 1 (2010): 1–28. http://dx.doi.org/10.1007/s10707-010-0111-6.

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41

NagaSaranya, N., and M. Hemalatha. "Estimation of Evolutionary Optimization Algorithm for Association Rule using Spatial Data Mining." International Journal of Computer Applications 51, no. 3 (2012): 1–5. http://dx.doi.org/10.5120/8019-8204.

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42

Soniya, Mudgal, and Parmar Mahesh. "Mining of the Correlations for Fatal Road Accident using Graph-based Fuzzified FP-Growth Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 279–83. https://doi.org/10.35940/ijeat.E9526.069520.

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Rapid population growth and economic activity have caused a continuous growth of motor vehicles and the increase in population and vehicle traffic injuries is increasing each day. Injury and death traffic accidents lead to not only significant economic losses however too severe mental & physical illness. Social issues have been created by the increasing road accident as a result of death and suffering and death. FP Growth Algorithm, Support Vector Machine (SVM) Cluster classification models and simple C-means clustering Algorithm formed Association laws. Some suggestions for safety driving were made based on data, association guidelines, classification model and obtained clusters. In this paper, we will attempt to address this problem by applying statistical study and FARS fatal accident DM algorithms. The findings suggest that the algorithm proposed is more efficient and faster than the algorithm of the previous research.

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Cortes-Ramirez, Javier, Darren Wraith, Peter D. Sly, and Paul Jagals. "Mapping the Morbidity Risk Associated with Coal Mining in Queensland, Australia." International Journal of Environmental Research and Public Health 19, no. 3 (2022): 1206. http://dx.doi.org/10.3390/ijerph19031206.

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The populations in the vicinity of surface coal mining activities have a higher risk of morbidity due to diseases, such as cardiovascular, respiratory and hypertensive diseases, as well as cancer and diabetes mellitus. Despite the large and historical volume of coal production in Queensland, the main Australian coal mining state, there is little research on the association of coal mining exposures with morbidity in non-occupational populations in this region. This study explored the association of coal production (Gross Raw Output—GRO) with hospitalisations due to six disease groups in Queensland using a Bayesian spatial hierarchical analysis and considering the spatial distribution of the Local Government Areas (LGAs). There is a positive association of GRO with hospitalisations due to circulatory diseases (1.022, 99% CI: 1.002–1.043) and respiratory diseases (1.031, 95% CI: 1.001–1.062) for the whole of Queensland. A higher risk of circulatory, respiratory and chronic lower respiratory diseases is found in LGAs in northwest and central Queensland; and a higher risk of hypertensive diseases, diabetes mellitus and lung cancer is found in LGAs in north, west, and north and southeast Queensland, respectively. These findings can be used to support public health strategies to protect communities at risk. Further research is needed to identify the causal links between coal mining and morbidity in non-occupational populations in Queensland.

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Liang, Li Heng, Li Xin Xing, Tong Lin Li, Hong Yan Jiang, and Li Jun Jiang. "Study on Geomorphologic Spatial Information Mining and Application." Advanced Materials Research 250-253 (May 2011): 1236–42. http://dx.doi.org/10.4028/www.scientific.net/amr.250-253.1236.

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Digital Elevation Models (DEM) implies numbers of geomorphologic spatial information. It not only includes the three-dimensional coordinate but also has unique texture information, which can describe the ‘true’ land surface adequately at relation of neighbors (plan) and relative (amplitude). We will use a method to study the wavelength characters by data mining and distribution of slope and local relief on the altitude steps through a local window. The Shuttle Radar Topography Mission (SRTM) collect detailed Digital Elevation Models(DEM) data between 60°N and 57°S, 80 percent for all land masses, and it provides reliable, high precision surface elevation data for us, suits to analyze efficiently landscape pattern. SRTM-DEM data simulate three-dimensional land surface with regular gridded matrix, and these discrete points are fit for spatial neighbors’ analysis and statistics, and convenient to geomorphologic pattern computation and analysis in digital computer. Geomorphologic pattern is influenced by Physical properties and human activities in a most direct way, but whilst it record numbers of geological evolution evidence, and these records provide some important information for climate change, geological and geographical processes and ecological environment researches in science. In this study, making the whole Jilin province as study object, we propose a fourth-order equation to approximate land as a continuous curved surface, association neighbors’ analysis method, utilize digital elevation matrix to validate an optimal statistic window, and subsequent study the area spatial distribution by parameterization and classification, get a satisfactory effect.

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Jayababu, Y., G. P. S. Varma, and A. Govardhan. "Mining Spatial Association Rules to Automatic Grouping of Spatial Data Objects Using Multiple Kernel-Based Probabilistic Clustering." Journal of Intelligent Systems 26, no. 3 (2017): 561–72. http://dx.doi.org/10.1515/jisys-2016-0044.

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AbstractWith the extensive application of spatial databases to various fields ranging from remote sensing to geographical information systems, computer cartography, environmental assessment, and planning, discovery of interesting and hidden knowledge in the spatial databases is a considerable chore for classifying and using the spatial data and knowledge bases. The literature presents different spatial data mining methods to mine knowledge from spatial databases. In this paper, spatial association rules are mined to automatic grouping of spatial data objects using a candidate generation process with three constraint measures, such as support, confidence, and lift. Then, the proposed multiple kernel-based probabilistic clustering is applied to the associate vector to further group the spatial data objects. Here, membership probability based on probabilistic distance is used with multiple kernels, where exponential and tangential kernel functions are utilized. The performance of the proposed method is analyzed with three data sets of three different geometry types using the number of rules and clustering accuracy. From the experimentation, the results proved that the proposed multi-kernel probabilistic clustering algorithm achieved better accuracy as compared with the existing probabilistic clustering.

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Sandra Isioma Erue. "Geo-epidemiological mapping of respiratory disease prevalence with mining dust exposure in Ogun State, Nigeria." World Journal of Advanced Research and Reviews 15, no. 3 (2022): 595–608. https://doi.org/10.30574/wjarr.2022.15.3.0906.

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Mining activities are significant contributors to ambient particulate matter (PM) emissions, which pose serious public health risks, especially in rapidly urbanizing regions. This study investigates the spatial correlation between PM exposure and respiratory disease prevalence in quarry-dense communities of Ogun State, Nigeria. A cross-sectional ecological design was employed, integrating gravimetric PM₁₀ and PM₂.₅ sampling, meteorological observations, and retrospective health data from 2018 to 2023. Twelve sampling sites across Ewekoro and Sagamu Local Government Areas (LGAs) were selected based on quarry proximity and population density. Spatial interpolation (Kriging), hotspot detection (Getis-Ord Gi*), Local Indicators of Spatial Association (LISA), and regression models (OLS and GWR) were applied to assess spatial patterns and exposure-response relationships. A decision tree model was also developed to predict high-risk communities. PM₁₀ and PM₂.₅ levels peaked during the dry season, with concentrations exceeding WHO guidelines across multiple sites. Respiratory diseases were most prevalent among adults aged 25–64 and children aged 5–14, with Itori, Papalanto, and Emuren communities showing the highest incidence. Significant spatial clustering of disease was confirmed through Gi* and LISA analyses. GWR outperformed OLS in modeling PM₂.₅-disease relationships (Adjusted R² = 0.74), revealing stronger associations in communities nearest to quarry operations. The decision tree identified PM₂.₅ >110 µg/m³ and residence within 2.5 km of a quarry as key predictors of elevated risk. This study demonstrates strong spatial associations between particulate pollution from mining and respiratory disease burden in quarry-adjacent communities. Findings support the implementation of spatial buffer zones, local air quality surveillance, and integrated health monitoring systems to mitigate environmental health risks in vulnerable populations.

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Wang, Xiaoxuan, Peijie Jin, Wen Xiong, and Song Gao. "Mining Highly Visited Co-Location Patterns Based on Minimum Visitor Similarity Constraints." Electronics 12, no. 18 (2023): 3961. http://dx.doi.org/10.3390/electronics12183961.

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Spatial co-location pattern is a subset of spatial features which shows association relationships based on the spatial neighborhoods. Because the previous prevalence measurements of a co-location pattern have not considered the visited information of spatial instances, co-location patterns do not reflect the social connections (such as their spatial instances are constantly visited by common or similar moving objects) between spatial features. In this paper, a special type of co-location pattern, “Highly visited co-location patterns”, is proposed, which considers the spatial proximity and visitor similarity of spatial features at the same time. A new measurement, “Minimum visitor similarity”, has been proposed to reflect the visitor similarity of co-location patterns. By discussing the properties of the minimum visitor similarity, we propose an efficient algorithm to mine the highly visited co-locations and give two pruning strategies to improve the efficiency of the algorithm. Finally, extensive experiments on YELP and Foursquare datasets prove the practicability and efficiency of the proposed algorithm, and we define a “Social Entropy” to prove that spatial features in the co-locations we mined have stronger social connections.

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Mbarek, Abdelilah, Mouna Jiber, Ali Yahyaouy, and Abdelouahed Sabri. "Accident black spots identification based on association rule mining." Bulletin of Electrical Engineering and Informatics 13, no. 3 (2024): 2075–85. http://dx.doi.org/10.11591/eei.v13i3.6135.

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This paper presents an analytical approach to identifying the important characteristics of accident black spots on Moroccan rural roads. An association rule mining method is applied to extract road spatial characteristics associated with fatal accidents. The weighted severity index was calculated for each section, which was then used to determine the severity levels of black spots. The apriori algorithm is applied to find the correlation between road characteristics and the severity levels of black spots. Then, a general rule selection method is proposed to identify the rules strongly associated with each severity level. The results show that the proposed approach is effective in identifying the most important factors contributing to accidents. Furthermore, it shows that the combination of several road characteristics, such as road width, road surface, and bridge presence, may contribute to fatal accidents. The general rule selection found that wet, bad surfaces, and narrow shoulders were significantly associated with accidents on rural roads. The findings of the present study can help develop effective strategies to reduce road accidents and thus improve road safety in the country.

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Xuewu, Zhang. "Temporal and Spatial Association Rules Strong Mining Algorithm Based on Hierarchical Reasoning Parameters." International Journal of Database Theory and Application 10, no. 1 (2017): 57–66. http://dx.doi.org/10.14257/ijdta.2017.10.1.06.

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Kumar, Raghvendra, Le Hoang Son, Sudan Jha, Mamta Mittal, and Lalit Mohan Goyal. "Spatial data analysis using association rule mining in distributed environments: a privacy prospect." Spatial Information Research 26, no. 6 (2018): 629–38. http://dx.doi.org/10.1007/s41324-018-0207-x.

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