Extended High Dimensional Clustering using Iterative Two Dimensional Projection Filtering


The KIPS Transactions:PartD, Vol. 8, No. 5, pp. 573-580, Oct. 2001
10.3745/KIPSTD.2001.8.5.573,   PDF Download:

Abstract

The large amounts of high dimensional data contains a significant amount of noises by its own sparsity, which adds difficulties in high dimensional clustering. The CLIP is developed as a clustering algorithm to support characteristics of the high dimensional data. The CLIP is based on the incremental one dimensional projection on each axis and find product sets of one dimensional clusters. These product sets contain not only all high dimensional clusters but also they may contain noises. In this paper, we propose extended CLIP algorithm which refines the product sets that contain clusters. We remove high dimensional noises by applying two dimensional projections iteratively on the already found product sets by CLIP. To evaluate the performance of extended algorithm, we demonstrate its effectiveness through a series of experiments on synthetic data sets.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
H. M. Lee and Y. B. Park, "Extended High Dimensional Clustering using Iterative Two Dimensional Projection Filtering," The KIPS Transactions:PartD, vol. 8, no. 5, pp. 573-580, 2001. DOI: 10.3745/KIPSTD.2001.8.5.573.

[ACM Style]
Hye Myung Lee and Young Bae Park. 2001. Extended High Dimensional Clustering using Iterative Two Dimensional Projection Filtering. The KIPS Transactions:PartD, 8, 5, (2001), 573-580. DOI: 10.3745/KIPSTD.2001.8.5.573.