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Comparative Analsis Of Spatial Co Location Pattern Discovery Algorithms

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A branch of spatial association rules mining; co-location mining is a significant member of the spatial data mining arena. The problem of spatial association rules mining was first accessible in Koperski & Han(1995), in which the authors anticipated a reference feature centric model Shekhar et al (2001)to determine spatial association rules. The solicitation fields that focus on specific BSF such as cancer are more appropriate to reference feature centric model. Investigators in this field try to find CLPs between this BSP and other task associated features like asbestos or other substances.

This model is generally centered on the perception of neighborhood relationship to hypothesis the transactions from delivered datasets. It also practices the dimensions such as maintenance and confidence to display the amount of interestingness. Spatial instances of other spatial features nearby to instances of the reference feature are repossessed. Each set of spatial instances that are neighbors to an instance of the reference feature is deliberated as a transaction, and traditional association rules mining algorithms are pragmatic to determine association rules associated to this reference feature. The basic indication behind this tactic is changing the spatial datasets to nonspatial transaction datasets, and applying traditional data mining techniques. This idea was general in the early phases of spatial data mining research. However, this method has a major disadvantage in that not every application has a strong reference feature. For example, in the universal field of ecology, scientists might be concerned in the co-location patterns between biological species deprived of special interest in a definite species. Using this method, scientists would need to use each species as the reference feature and do multiple mining. Such a strategy would upshot in too much human participation and would be likely to slip some interesting patterns. Morimoto (2001) proposes an Apriori-like algorithm for co-location mining.

The classification of co-location patterns in this paper, however, is different from the one we are interested in. This method uses the number of instances as the prevalence measure of the co-location patterns. However, this prevalence measure is not essentially anti-monotone because a distinct spatial instance might contribute in numerous instances of a co-location pattern. To make the prevalence measure anti-monotone such that the pruning works, this approach adds a restriction that each spatial instance can participate in only one instance of a co-location pattern. This restraint leads to another problem: the dissimilar ordering of spatial features and the phases of mining process itself leads to dissimilar co-location instances and consequently the prevalence of a co-location pattern intended might fluctuate, too. Another problem with this method is that each co-location instance is represented using its centroid. For a certain co-location instance, this algorithm will only join it with the nearest instance of a new feature type, according to the distance between the centroid and instances of new feature types. So, the co-location instances discovered do not necessarily form a clique.

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GradesFixer, 2019. Comparative Analsis Of Spatial Co Location Pattern Discovery Algorithms. [online] Available at: <https://gradesfixer.com/free-essay-examples/comparative-analsis-of-spatial-co-location-pattern-discovery-algorithms/> [Accessed 7 July 2020].
GradesFixer. Comparative Analsis Of Spatial Co Location Pattern Discovery Algorithms [Internet]. GradesFixer; 2019 [cited 2019 November 26]. Available from: https://gradesfixer.com/free-essay-examples/comparative-analsis-of-spatial-co-location-pattern-discovery-algorithms/
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