发明名称 |
DATA DECOMPOSITION/REDUCTION METHOD FOR VISUALIZING DATA CLUSTERS/SUB-CLUSTERS |
摘要 |
Higher dimensionality data is subject to a hierarchical visualization to allow the complete data set to be visualized in a top-down hierarchy in terms of clusters and sub-clusters at deeper levels. The data set is subject to standard finite normal mixture models and probabilistic principal component projections, the parameters of which are estimated using the expectation-maximization and principal component analysis under the Akaike Information Criteria (AIC) and the Minimum Description Length (MDL) criteria. The high-dimension raw data is subject to processing using principal component analysis to reveal the dominant distribution of the data at a first level. Thereafter, the so-processed information is further processed to reveal sub-clusters within the primary clusters. The various clusters and sub-clusters at the various hierarchical levels are subject to visual projection to reveal the underlying structure. The inventive schema has utility in all applications in which high-dimensionality multi-variate data is to be reduced to a two- or theree-dimensional projection space to allow visual exploration of the underlying structure of the data set.
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申请公布号 |
WO0016250(A1) |
申请公布日期 |
2000.03.23 |
申请号 |
WO1999US21363 |
申请日期 |
1999.09.17 |
申请人 |
THE CATHOLIC UNIVERSITY OF AMERICA |
发明人 |
WANG, JOSEPH, Y. |
分类号 |
G06F17/18;G06F17/30;G06K9/62;G06T7/00;(IPC1-7):G06K9/62 |
主分类号 |
G06F17/18 |
代理机构 |
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