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Discovering the meaningful patterns and trends out of large datasets needs a very special attention now a day, and one of the most prevalent and widely studied problems in this area is the detection and formation of clusters accurately and correctly. Previous papers on this field do not meet the problem of 3D spatial datasets with minimization of Input Parameters. The objective of this paper is to present a Tetrahedron-density based clustering technique for large 3D datasets which, the authors have named as 3D-CATD (Three Dimensional-Clustering Algorithm using Tetrahedron Density), for efficient clustering of 3D spatial data. This algorithm is capable of identifying embedded clusters of arbitrary shapes as well as multi-density clusters over large 3D spatial datasets.
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