Based on the principle of cellular automata in artificial life, an artificial Ants Sleeping Model (ASM) and an ant algorithm for cluster analysis (A⁴C) are presented. Inspired by the behaviors of gregarious ant colonies, we use the ant agent to represent data object. In ASM, each ant has two states: sleeping state and active state. The ant's state is controlled by a function of the ant's fitness to the environment it locates and a probability for the ants becoming active. The state of an ant is determined only by its local information. By moving dynamically, the ants form different subgroups adaptively, and hence the data objects they represent are clustered. Experimental results show that the A⁴C algorithm on ASM is significantly better than other clustering methods in terms of both speed and quality. It is adaptive, robust and efficient, achieving high autonomy, simplicity and efficiency.