Clustering in scientific data mining based on grid and iterative method

The clustering is an important part of the scientific data mining.Among the various algorithms a main class is based on the "distance".The "K-means" and "k-medoids" are two of these kinds.However these algorithms are inefficient when dealing with the large data sets and data sets of high-dimension.This algorithm differs much from the above ones and it takes a totally different approach called a grid and density based algorithm.It can automatically find out the subspaces containing interesting patterns and discover all clusters in that subspace and it performs well when dealing with the high-dimensional data.