Generation and Analysis of Tree Structures Based on Association Rules and Hierarchical Clustering

Detailed inspection of transactional data can reveal various useful information, in which of special importance are relationships between transaction elements. Hierarchical clustering coupled with specific distance measures reveal those relationships from one angle. Additionally, association rules - a natural method of inspecting transactional data - is able to reveal relationships between each pair of transaction elements. With transactional data modulation and multiple usages of this method a tree-like structure can be created. This paper concerns the interpretation of resulting structures for each method as well as providing comparison between them. For each algorithm mathematical base is introduced along with an explanation of result interpretation. Performances of two methods are compared and examined on a real life data set.