Clustering is a promising application area for many fields including statistics

pattern recognition

datamining

etc.The effectiveness and efficiency of existing clustering techniques

however

is somewhat limited

owing to the huge amounts data collected in databases.According the theory of fields in physics

a hierarchical clustering method based on data fields is presented.The basic idea is that the field models is introduced to describe the virtual interaction among data objects in data space and the hierarchical partitioning of the original dataset is then performed by iteratively simulating the interaction and movement of the data objects in the fields.Experimental results show that the proposed approach not only enjoys favorite clustering quality and requires no careful parameters tuning

but also has a time complexity approximately linear with respect to the size of dataset.