High-Dimensional Clustering Technique using Incremental Projection
Hye-Myung Lee, Young-Bae Park · 2001
Most of clustering algorithms data to degenerate rapidly on high dimensional spaces. Moreover, high dimensional data often contain a significant a significant of noise. which causes additional ineffectiveness of algorithms. Therefore it is necessary to develop algorithms adapted to the structure and characteristics of the high dimensional data. In this paper, we propose a clustering algorithms CLIP using the projection The CLIP is designed to overcome efficiency and/or effectiveness problems on high dimensional clustering and it is the is based on clustering on each one dimensional subspace but we use the incremental projection to recover high dimensional cluster and to reduce the computational cost significantly at time To evaluate the performance of CLIP we demonstrate is efficiency and effectiveness through a series of experiments on synthetic data sets.