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IGARSS
2009
15 years 4 months ago
Reducing the Dimensionality of Hyperspectral Data using Diffusion Maps
We examine the analysis of hyperspectral data produced by the Hyperspectral Core Imager of AngloGold Ashanti. The dimension of the data is reduced using diffusion maps and the dat...
Luis du Plessis, Steven Damelin, Michael Sears
KDD
2003
ACM
191views Data Mining» more  KDD 2003»
16 years 6 months ago
Assessment and pruning of hierarchical model based clustering
The goal of clustering is to identify distinct groups in a dataset. The basic idea of model-based clustering is to approximate the data density by a mixture model, typically a mix...
Jeremy Tantrum, Alejandro Murua, Werner Stuetzle
KDD
2004
ACM
150views Data Mining» more  KDD 2004»
16 years 6 months ago
A framework for ontology-driven subspace clustering
Traditional clustering is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. While domain knowledge is always the bes...
Jinze Liu, Wei Wang 0010, Jiong Yang
SIGMOD
2001
ACM
200views Database» more  SIGMOD 2001»
16 years 6 months ago
Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering
In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or ra...
Markus M. Breunig, Hans-Peter Kriegel, Peer Kr&oum...
ACISICIS
2007
IEEE
16 years 22 days ago
A Modified K-means Algorithm for Noise Reduction in Optical Motion Capture Data
This paper presents a modified K-means algorithm that can be used for removing noise in multicolor motion capture image sequences. These images have been produced using the Illumi...
Jan Carlo Barca, Grace W. Rumantir