Decisions made by domain experts, such as in healthcare and market research, are influenced by the conditional co-occurrence of different events. Learning about conditional co-occurrence is also beneficial for non-experts-the general public. By understanding the co-occurrences of diseases, it is easier to understand which diseases individuals are susceptible to. However, co-occurrence data is often complex. In order for a public understanding of conditional co-occurrence, there needs to be a simpler form to convey such complex information. We introduce an organic visual metaphor, which can provide a summary of the conditional co-occurrences within a large set of items and is accessible to the public with its organic shape. We develop a prototype application offering not only an overview for users to gain insights on how co-occurrence patterns evolve based on user-defined criteria (e.g., how do sex and age affect likelihood), but also functionality to explore the hierarchical data in-depth. We conducted two case studies with this prototype to demonstrate the effectiveness of our design.