Visualization is the post processing stage in Knowledge Discovery Process (KDP) to simplify the process of understanding, abstraction, and diminishing the size of mined information, patterns, and/or knowledge. Pre-mining and mining stages of KDP seem as preprocessing steps for visualization engine. Visualization is a complex process because it needs a formal definition of complicated rules to translate large volumes of data into graphic formats. In this research, the term Visual KDP, VKDP was suggested, in which the benefits of visualization techniques have been utilized before, during, and after the data mining stage. To prove the validity and applicability of VKDP approach it is applied to the most important task of Data Mining; Association Rules Mining. The process of finding the appropriate visualization techniques is not a trivial one. Therefore, many visualization techniques are proposed for different levels of Association Rule (AR) mining, i.e., for database under mining, intermediate result or mining level, and mined rule level. Bipartite graph is proposed as a new technique to visualize the database under mining in addition to many variations in horizontal and vertical layouts. Also, networks of concepts are proposed as a new visualization technique to visualize the mined frequent itemsets, while the two-dimensional matrix, directed graph, and rule-item approach are adopted as visualization techniques to visualize the mined rules.