Multilevel association rule mining in distributed environment plays an important role in big data analysis for making marketing strategy. Multilevel association rule provides more significant information than single level rule, and also discovers the conceptual hierarchy of knowledge from the hierarchical dataset. In this era of internet, various online marketing sites and social networking sites are generating enormous amount of data so that it becomes very difficult to process and analyze it using conventional approaches as it consumes more time. This paper overcomes the computing limitation of single node by distributing the task on multi-node cluster. The proposed method initially extracts multilevel association rules including level-crossing for each zone using distributed multilevel frequent pattern mining algorithm (DMFPM). These generated multilevel association rules are so large that it becomes complex to analyze it. Thus, MapReduce based multilevel consistent and inconsistent rule detection (MR-MCIRD) algorithm is proposed to detect the consistent and inconsistent multilevel rules from big hierarchical data which provide useful and actionable knowledge to the domain experts. These pruned interesting rules also give useful knowledge for better marketing strategy. The extracted multilevel consistent and inconsistent rules are evaluated and compared based on different interestingness measures presented together with experimental results that lead to the final conclusions.

分布式环境中的多级关联规则挖掘在大数据分析中制定营销策略起着重要作用。与单级规则相比，多级关联规则提供了更重要的信息，并且还从层次数据集中发现了知识的概念层次。在这个互联网时代，各种在线营销网站和社交网站都在生成大量数据，因此，使用传统方法来处理和分析数据将花费更多时间，因此变得非常困难。通过在多节点集群上分配任务，克服了单节点的计算限制。所提出的方法最初使用分布式多级频繁模式挖掘算法（DMFPM）为每个区域提取包括级别交叉的多级关联规则。这些生成的多级关联规则是如此之大，以至于对其进行分析变得很复杂。因此，提出了一种基于MapReduce的多级一致性和不一致规则检测算法（MR-MCIRD），用于从大型分层数据中检测出一致性和不一致的多级规则，从而为领域专家提供有用和可操作的知识。这些经过修剪的有趣规则还为更好的营销策略提供了有用的知识。基于提出的不同兴趣度度量以及得出最终结论的实验结果，对提取的多级一致和不一致规则进行评估和比较。提出了一种基于MapReduce的多级一致性和不一致规则检测算法，以从大型分层数据中检测出一致性和不一致的多层规则，为领域专家提供了有用的和可操作的知识。这些经过修剪的有趣规则还为更好的营销策略提供了有用的知识。基于提出的不同兴趣度度量以及得出最终结论的实验结果，对提取的多级一致和不一致规则进行评估和比较。提出了一种基于MapReduce的多级一致性和不一致规则检测算法，以从大型分层数据中检测出一致性和不一致的多层规则，为领域专家提供了有用的和可操作的知识。这些经过修剪的有趣规则还为更好的营销策略提供了有用的知识。基于提出的不同兴趣度度量以及得出最终结论的实验结果，对提取的多级一致和不一致规则进行评估和比较。