  Advances made to the traditional clustering algorithms solves the various
problems such as curse of dimensionality and sparsity of data for multiple
attributes. The traditional H-K clustering algorithm can solve the randomness
and apriority of the initial centers of K-means clustering algorithm. But when
we apply it to high dimensional data it causes the dimensional disaster problem
due to high computational complexity. All the advanced clustering algorithms
like subspace and ensemble clustering algorithms improve the performance for
clustering high dimension dataset from different aspects in different extent.
Still these algorithms will improve the performance form a single perspective.
The objective of the proposed model is to improve the performance of
traditional H-K clustering and overcome the limitations such as high
computational complexity and poor accuracy for high dimensional data by
combining the three different approaches of clustering algorithm as subspace
clustering algorithm and ensemble clustering algorithm with H-K clustering
algorithm.
