Advances made to the traditional clustering algorithms solves the various problems such as curse ofdimensionality and sparsity of data for multiple attributes. The traditional H-K clustering algorithm cansolve the randomness and apriority of the initial centers of K-means clustering algorithm. But when weapply it to high dimensional data it causes the dimensional disaster problem due to high computationalcomplexity. All the advanced clustering algorithms like subspace and ensemble clustering algorithmsimprove 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 theproposed model is to improve the performance of traditional H-K clustering and overcome the limitationssuch as high computational complexity and poor accuracy for high dimensional data by combining thethree different approaches of clustering algorithm as subspace clustering algorithm and ensembleclustering algorithm with H-K clustering algorithm.
展开▼
