  The description of complex configuration is a difficult issue. We present a
powerful technique for cluster identification and characterization. The scheme
is designed to treat with and analyze the experimental and/or simulation data
from various methods. Main steps are as follows. We first divide the space
using face or volume elements from discrete points. Then, combine the elements
with the same and/or similar properties to construct clusters with special
physical characterizations. In the algorithm, we adopt administrative structure
of hierarchy-tree for spatial bodies such as points, lines, faces, blocks, and
clusters. Two fast search algorithms with the complexity are realized. The
establishing of the hierarchy-tree and the fast searching of spatial bodies are
general, which are independent of spatial dimensions. Therefore, it is easy to
extend the skill to other fields. As a verification and validation, we treated
with and analyzed some two-dimensional and three-dimensional random data.
