Association rule mining is one of the most popular data mining techniques, and its aim is to extract the association rules, the cause-and-effect relations between the items, from the given transaction data. Several algorithms such as apriori and its variants have been developed in order to extract the association rules in efficient way, however, they often produce the plethora of the association rules that is difficult for the analyzers to interpret and exploit. To address this issue, this paper aims to propose a visualization method called structured association map. The structured association map is a variant of the well known cluster hear map, and it focuses on ordering the items in more meaningful way. The structured association map and the cluster heat map have in common that the dendrogram obtained by hierarchical clustering is appended to the matrix for data visualization and the items are ordered according to the dendrogram. On the contrary, the primary difference between the two visualization methods lies in the way the hierarchy of the column items is generated. In structured association map, the row items are ordered at first and their order is considered in ordering the column items, while the row items and column items of the cluster heat map are ordered in similar manner. Consequently, the structured association map can represent both antecedents and consequents of the association rules in more effective way, and it is expected to help the analyzers to understand the structure of the extracted association rules more conveniently.