ABSTRACT

Association rule mining is typically used to uncover the enthralling interdependencies between the set of variables and reveals the hidden pattern within the dataset. The associations are identified based on co-occurring variables with high frequencies. These associations can be positive (A→B) or negative (A→⌐B). The number of these association rules in larger databases are considerably higher which restricted the extraction of valuable insights from the dataset. Some rule pruning strategies are used to reduce the number of rules that can sometimes miss an important, or include an unimportant rule into the final rule set because of not considering the context of the rule. Context-based positive and negative association rule mining (CBPNARM) for the first time included context variable in the algorithms of association rule mining for selection/ de-selection of such rules. In CBPNARM, the selection of context variable and its range of values are done by the user/expert of the system which demands unwanted user interaction and may add some bias to the results. This paper proposes a method to automate the selection of context variable and selection of its value range. The context variable is chosen by using the diversity index and chi-square test, and the range of values for the context variable is set by using box plot analysis. The proposed method on top of it added conditional-probability increment ratio (CPIR) for further pruning uninteresting rules. Experiments show the system can select the context variable automatically and set the right range for the selected context variable. The performance of the proposed method is compared with CBPNARM and other state of the art methods.

摘要

关联规则挖掘通常用于揭示变量集之间令人着迷的相互依赖关系，并揭示数据集中隐藏的模式。这些关联是根据高频共现变量来识别的。这些关联可以是正面的 (A→B) 或负面的 (A→⌐B)。大型数据库中这些关联规则的数量要多得多，这限制了从数据集中提取有价值的见解。一些规则剪枝策略用于减少有时会错过重要规则的规则数量，或者由于不考虑规则的上下文而将不重要的规则包含到最终规则集中。基于上下文的正负关联规则挖掘（CBPNARM）首次将上下文变量包含在关联规则挖掘算法中，用于选择/取消选择此类规则。在 CBPNARM 中，上下文变量及其取值范围的选择是由系统的用户/专家完成的，这需要不需要的用户交互，并且可能会给结果增加一些偏差。本文提出了一种自动选择上下文变量及其取值范围的方法。通过使用多样性指数和卡方检验选择上下文变量，并通过使用箱线图分析设置上下文变量的值范围。所提出的方法在此基础上添加了条件概率增量比 (CPIR) 以进一步修剪不感兴趣的规则。实验表明，系统可以自动选择上下文变量，并为选择的上下文变量设置合适的范围。将所提出方法的性能与 CBPNARM 和其他最先进的方法进行了比较。