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Context based positive and negative spatio-temporal association rule mining

机译：基于上下文的正负时空关联规则挖掘

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摘要

This paper proposes a new approach to mine context based positive and negative spatial association rules as they might be applied to hydrocarbon prospection. Many researchers are currently using an Apriori algorithm on spatial databases but this algorithm does not utilize the strengths of positive and negative association rules and of time series analysis, hence it misses the discovery of very interesting and useful associations present in the data. In dense spatial databases, the number of negative association rules is much higher compared to the positive rules which need exploitation. Using positive and negative association rule discovery and then pruning out the uninteresting rules consumes resources without much improvement in the overall accuracy of the knowledge discovery process. The associations among different objects and lattices are strongly dependent upon the context, particularly where context is the state of entity, environment or action. We propose an approach to spatial association rule mining from datasets projected at a temporal bar in which the contextual situation is considered while generating positive and negative frequent itemsets. An extended algorithm based on the Apriori approach is developed and compared with existing spatial association rule algorithms. The algorithm for positive and negative association rule mining is based on Apriori algorithm which is further extended to include context variable and simulate temporal series spatial inputs. The numerical evaluation shows that our algorithm is more efficient at generating specific, reliable and robust information than traditional algorithms.
机译：本文提出了一种新的方法来挖掘基于上下文的正负空间关联规则，因为它们可能会应用于油气勘探。当前，许多研究人员正在空间数据库上使用Apriori算法，但是该算法没有利用正负关联规则和时间序列分析的优势，因此错过了数据中存在的非常有趣且有用的关联的发现。在密集的空间数据库中，否定关联规则的数量比需要利用的积极规则要多得多。使用肯定和否定关联规则发现然后删除不感兴趣的规则会消耗资源，而不会大大提高知识发现过程的整体准确性。不同对象和格之间的关联在很大程度上取决于上下文，尤其是在上下文是实体，环境或动作的状态的情况下。我们提出了一种从在时间条上投影的数据集进行空间关联规则挖掘的方法，其中考虑了上下文情况，同时生成了积极和消极的频繁项集。开发了一种基于Apriori方法的扩展算法，并将其与现有的空间关联规则算法进行比较。用于正负关联规则挖掘的算法基于Apriori算法，该算法进一步扩展为包括上下文变量并模拟时间序列空间输入。数值评估表明，与传统算法相比，我们的算法在生成特定，可靠和健壮的信息方面效率更高。

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