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Advances made to the traditional clustering algorithms solves the variousproblems such as curse of dimensionality and sparsity of data for multipleattributes. The traditional H-K clustering algorithm can solve the randomnessand apriority of the initial centers of K-means clustering algorithm. But whenwe apply it to high dimensional data it causes the dimensional disaster problemdue to high computational complexity. All the advanced clustering algorithmslike subspace and ensemble clustering algorithms improve the performance forclustering 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 oftraditional H-K clustering and overcome the limitations such as highcomputational complexity and poor accuracy for high dimensional data bycombining the three different approaches of clustering algorithm as subspaceclustering algorithm and ensemble clustering algorithm with H-K clusteringalgorithm.

A H-K Clustering Algorithm For High Dimensional Data Using Ensemble Learning