Most of existing clustering algorithms are designed to discover snapshot clusters that reflect only the current status of a database. Snapshot clusters do not reveal the fact that clusters may either persist over a period of time, or slowly fade away as other clusters may gradually develop. Predicting dynamic cluster evolutions and their occurring periods are important because this information can guide users to prepare appropriate actions toward the right areas during the right time for the most effective results. In this paper we developed a simple but effective approach in predicting the future distance among object pairs. Objects that will be close in distance over different periods of time are then processed to discover density-based clusters that may occur or change over time.