NEC: a hierarchical agglomerative clustering based on fisher and negentropy information
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Probabilistic principal surface classifier
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
ADA'04 Proceedings of the 3rd international conference on Astronomical Data Analysis
| Hi-index | 0.00 |
The recent technological advances are producing huge data sets in almost all fields of scientific research, from astronomy to genetics. Although each research field often requires ad-hoc, fine tuned, procedures to properly exploit all the available information inherently present in the data, there is an urgent need for a new generation of general computational theories and tools capable to boost most human activities of data analysis. Here we propose Probabilistic Principal Surfaces (PPS) as an effective high-D data visualization and clustering tool for data mining applications, emphasizing its flexibility and generality of use in data-rich field. In order to better illustrate the potentialities of the method, we also provide a real world case-study by discussing the use of PPS for the analysis of yeast gene expression levels from microarray chips.