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

Advances made to the traditional clustering algorithms solve 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 the authors 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.

Resource Details

Provided by:
Academy & Industry Research Collaboration Center
Data Management