The emerging field of visual analytics seeks to address the needs of exploratory discovery in big data. The approach is to marry the big data processing capabilities of analytics with the human intuitive capabilities of interactive visualization. The rationale is that data is too large for purely visual methods, requiring the use of data processing and mining; yet, the desired tasks are too exploratory for purely analytical methods, requiring the involvement of human analysts, using visualization as a medium for human interaction with the data. This approach must be situated within an understanding of human cognitive reasoning processes. Thus, visual analytics research necessitates an interdisciplinary approach. Existing techniques are inefficient to support exploring coordinated relationships, and few attempt to adapt biclusters to facilitate this by following the design framework. Thus, it is still challenging to design a technique that can take advantage of biclusters and make them usable to support coordinated relationship explorations. In this project the system proposed a BiSet, a visual analytics technique to support interactive exploration of coordinated relationships. In BiSet, we propose two important concepts to balance the key design trade-off: making bundles as first class objects and adding a new layer “in-between” lists to contain bundle objects. The former enables users to directly manipulate relationships and the latter helps to visually reveal membership of entities in two neighboring lists without duplicating entities. BiSet supports two ways of organizing information, which is also bidirectional. Users can organize the position of entities based on bundles, and vice versa.