HD-Eye: visual mining of high-dimensional data
Open Access
- 1 January 1999
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Computer Graphics and Applications
- Vol. 19 (5) , 22-31
- https://doi.org/10.1109/38.788795
Abstract
Clustering in high-dimensional databases poses an important problem. However, we can apply a number of different clustering algorithms to high-dimensional data. The authors consider how an advanced clustering algorithm combined with new visualization methods interactively clusters data more effectively. Experiments show these techniques improve the data mining process.Keywords
This publication has 27 references indexed in Scilit:
- Navigating large networks with hierarchiesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Efficient color histogram indexingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Parallel coordinates: a tool for visualizing multi-dimensional geometryPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Exploring N-dimensional databasesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Shape coding of multidimensional data on a microcomputer displayPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Visualizing network dataIEEE Transactions on Visualization and Computer Graphics, 1995
- Graphical fisheye viewsCommunications of the ACM, 1994
- VisDB: database exploration using multidimensional visualizationIEEE Computer Graphics and Applications, 1994
- Visual information seekingPublished by Association for Computing Machinery (ACM) ,1994
- Dynamic queries for information explorationPublished by Association for Computing Machinery (ACM) ,1992