  The problem of automatically clustering data is an age old problem. People
have created numerous algorithms to tackle this problem. The execution time of
any of this algorithm grows with the number of input points and the number of
cluster centers required. To reduce the number of input points we could average
the points locally and use the means or the local centers as the input for
clustering. However since the required number of local centers is very high,
running the clustering algorithm on the entire dataset to obtain these
representational points is very time consuming. To remedy this problem, in this
paper we are proposing two subclustering schemes where by we subdivide the
dataset into smaller sets and run the clustering algorithm on the smaller
datasets to obtain the required number of datapoints to run our clustering
algorithm with. As we are subdividing the given dataset, we could run
clustering algorithm on each smaller piece of the dataset in parallel. We found
that both parallel and serial execution of this method to be much faster than
the original clustering algorithm and error in running the clustering algorithm
on a reduced set to be very less.

Machine Learning