Association rule learning is a data mining technique that can capture relationships between pairs of entities in different domains. The goal of this research is to discover factors from data that can improve the precision, recall, and accuracy of association rules found using interestingness measures and frequent itemset mining. Such factors can be calibrated using validation data and applied t...

the performance of a depth-first Frequent Itemset Miming (FIM) algorithm is closely related to the total number of recursions which can be modeled as O(n), where k is the maximal recursion depth and n is the branching factor. Many existing approaches focus more on improving support counting rather than on decreasing n and k, which may lead to unsatisfactory performance as they grow. In this pap...

How to process missing attribute values is an important data preprocessing problem in data mining and knowledge discovery tasks. A commonly-used and naive solution to process data with missing attribute values is to ignore the instances which contain missing attribute values. This method may neglect important information within the data and a significant amount of data could be easily discarded...

We propose a general framework to formalize the problem of capturing the intensity of implication for association rules through statistical metrics. In this framework we present properties that influence the interestingness of a rule, analyze the conditions that lead a measure to perform a perfect prune at a time, and define a final proper order to sort the surviving rules. We will discuss why ...

the performance of a depth-first Frequent Itemset Miming (FIM) algorithm is closely related to the total number of recursions which can be modeled as O(n), where k is the maximal recursion depth and n is the branching factor. Many existing approaches focus more on improving support counting rather than on decreasing n and k, which may lead to unsatisfactory performance as they grow. In this pap...