Finding meaningful patterns and useful trends in large datasets has attracted considerable interest recently, and one of the most widely studied problems in this area is the identification and formation of clusters, or densely populated regions in a dataset. Prior work does not adequately address the problem of large datasets and minimization of I/O costs. The objective of this paper is to present a Triangle-density based clustering technique, which we have named as TDCT, for efficient clustering of spatial data. This algorithm is capable of identifying embedded clusters of arbitrary shapes as well as multi-density clusters over large spatial datasets. The Polygon approach is being used to perform the clustering where the number of points inside a triangle (triangle density) of a polygon is calculated using barycentric formulae. This is because of the fact that partitioning of the data set can be performed more efficiently in triangular shape than in any other polygonal shape due to its smaller space dimension. The ratio of number of points between two triangles can be found out which forms the basis of nested clustering. Experimental results are reported to establish the superiority of the technique in terms of cluster quality and complexity.