An algorithm of mining spatial topology association rules based on Apriori

Fang, Gang; Xiong, Jiang; Cheng-sheng, TU; Luo, A-Li

doi:10.1109/icicisys.2010.5658493

Cited by 3 publications

(8 citation statements)

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“…For the convenience of calculation, each group of data was standardized and split into averages and low-value areas [29] Given the nature of the Apriori algorithm, to analyze the association rules of landslide monitoring data, the association rules should be first formulated according to the algorithm logic. In this paper, any elements in the former (X) were randomly combined to formulate a series of association rules (see Table 2).…”

Section: Experiments and Analysismentioning

confidence: 99%

“…In this paper, any elements in the former (X) were randomly combined to formulate a series of association rules (see Table 2). The minimum support was set to 0.01 [29], and the confidence degree of each of the mentioned association rules should be calculated by the algorithm. On the whole, the association rule exhibiting a confidence above 0.7 is considered a strong association rule [17,29,30].…”

Section: Experiments and Analysismentioning

confidence: 99%

“…The minimum support was set to 0.01 [29], and the confidence degree of each of the mentioned association rules should be calculated by the algorithm. On the whole, the association rule exhibiting a confidence above 0.7 is considered a strong association rule [17,29,30]. Association rules with low confidence retain rules with a confidence level above 0.7 are deleted, and a series of useful information is calculated, laying a basis for landslide warning.…”

Section: Experiments and Analysismentioning

confidence: 99%

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Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm

Guo

Zuo

et al. 2019

Applied Sciences

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In the study of the mid-long-term early warning of landslide, the computational efficiency of the prediction model is critical to the timeliness of landslide prevention and control. Accordingly, enhancing the computational efficiency of the prediction model is of practical implication to the mid-long-term prevention and control of landslides. When the Apriori algorithm is adopted to analyze landslide data based on the MapReduce framework, numerous frequent item-sets will be generated, adversely affecting the computational efficiency. To enhance the computational efficiency of the prediction model, the IAprioriMR algorithm is proposed in this paper to enhance the efficiency of the Apriori algorithm based on the MapReduce framework by simplifying operations of the frequent item-sets. The computational efficiencies of the IAprioriMR algorithm and the original AprioriMR algorithm were compared and analyzed in the case of different data quantities and nodes, and then the efficiency of IAprioriMR algorithm was verified to be enhanced to some extent in processing large-scale data. To verify the feasibility of the proposed algorithm, the algorithm was employed in the mid-long-term early warning study of landslides in the Three Parallel Rivers. Under the same conditions, IAprioriMR algorithm of the same rule exhibited higher confidence than FP-Growth algorithm, which implied that IAprioriMR can achieve more accurate landslide prediction. This method is capable of technically supporting the prevention and control of landslides.

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Section: Experiments and Analysismentioning

confidence: 99%

Section: Experiments and Analysismentioning

confidence: 99%

Section: Experiments and Analysismentioning

confidence: 99%

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Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm

Guo

Zuo

et al. 2019

Applied Sciences

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“…Spatial association rules mining is a technique which mines the association rules in spatial databases by considering spatial properties and predicates [1,2,3] . One important problem is how to get those spatial relations that compose the spatial properties and predicates from the spatial objects, and translate the non-structured spatial relations to structural expression so that they can be mined with the non-spatial data together.…”

Section: Introductionmentioning

confidence: 99%

“…One important problem is how to get those spatial relations that compose the spatial properties and predicates from the spatial objects, and translate the non-structured spatial relations to structural expression so that they can be mined with the non-spatial data together. There are many researches about spatial association rules mining, Algorithm ARM involving the spatial relations such as direction, distance and topology has been well discussed in some literatures [2,[4][5][6][7] , whereas Fenzhen Su [8] centered on using spatial difference to express how spatial relations affect the interested spatial association rules we can get.As spatial topological relation is one of the most important spatial relations, many literatures about spatial association rules mining fasten on it and presented many ways to get the topological relation, such as RCC (Region Connection Calculus), the classical MBR (minimum bounding rectangle), 9-intersection model. Ickjai Lee etc.…”

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confidence: 99%

A Research on Spatial Topological Association Rules Mining

Chen

Song

Zhang

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission II, WG II/3 KEY WORDS:Geography, Research, Data mining, GIS, Algorithms ABSTRACT:Spatial association rules mining is a process of acquiring information and knowledge from large databases. Due to the nature of geographic space and the complexity of spatial objects and relations, the classical association rule mining methods are not suitable for the spatial association rule mining. Classical association rule mining treats all input data as independent, while spatial association rules often show high autocorrelation among nearby objects. The contiguous, adjacent and neighboring relations between spatial objects are important topological relations.In this paper a new approach based on topological predictions to discover spatial association rules is presented. First, we develop a fast method to get the topological relationship of spatial data with its algebraic structure. Then the interested spatial objects are selected. To find the interested spatial objects, topological relations combining with distance were used. In this step, the frequent topological predications are gained. Next, the attribute datasets of the selected interested spatial objects are mined with Apriori algorithm. Last, get the spatial topological association rules. The presented approach has been implemented and tested by the data of GDP per capita, railroads and roads in China in the year of 2005 at county level. The results of the experiments show that the approach is effective and valid. INTRODUCTIONSpatial association rules mining is a technique which mines the association rules in spatial databases by considering spatial properties and predicates [1,2,3] . One important problem is how to get those spatial relations that compose the spatial properties and predicates from the spatial objects, and translate the non-structured spatial relations to structural expression so that they can be mined with the non-spatial data together. There are many researches about spatial association rules mining, Algorithm ARM involving the spatial relations such as direction, distance and topology has been well discussed in some literatures [2,[4][5][6][7] , whereas Fenzhen Su [8] centered on using spatial difference to express how spatial relations affect the interested spatial association rules we can get.As spatial topological relation is one of the most important spatial relations, many literatures about spatial association rules mining fasten on it and presented many ways to get the topological relation, such as RCC (Region Connection Calculus), the classical MBR (minimum bounding rectangle), 9-intersection model. Ickjai Lee etc. Compared different RCC models' efficiency when used to get topological relations of a group of objects. MBR is a fast method to get the rough topological relations, so it is often used with other precise but expensive means. Eliseo Clementini etc.[3] put forward a method mining the spatial objects with uncertainty which uses the objects with a broad boundary to take the uncertainty of spatial information into account. The ...

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Research on Topology Association Rules Algorithm Based on Spatial Constraints

Tang

Zhang

2014

AMR

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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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An algorithm of mining spatial topology association rules based on Apriori

Cited by 3 publications

References 1 publication

Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm

Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm

A Research on Spatial Topological Association Rules Mining

Research on Topology Association Rules Algorithm Based on Spatial Constraints

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