### Abstract

Visual exploration has proven to be a powerful tool for multivariate data mining. Most visualization algorithms aim to find a projection from the data space down to a visually perceivable rendering space. To reveal all of the interesting aspects of complex data sets living in a high-dimensional space, a hierarchical visualization algorithm is introduced which allows the complete data set to be visualized at the top level, with clusters and subclusters of data points visualized at deeper levels. The methods involve multiple use of standard finite normal mixture models and probabilistic principal component projections, whose parameters are estimated using the expectation-maximization and principal component neural networks under the information theoretic criteria. We demonstrate the principle of the approach on two three-dimensional synthetic data sets.

Original language | English (US) |
---|---|

Pages | 497-506 |

Number of pages | 10 |

State | Published - Dec 1 1999 |

Externally published | Yes |

Event | Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99) - Madison, WI, USA Duration: Aug 23 1999 → Aug 25 1999 |

### Other

Other | Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99) |
---|---|

City | Madison, WI, USA |

Period | 8/23/99 → 8/25/99 |

### All Science Journal Classification (ASJC) codes

- Signal Processing
- Software
- Electrical and Electronic Engineering

## Fingerprint Dive into the research topics of 'Hierarchy of probabilistic principal component subspaces for data mining'. Together they form a unique fingerprint.

## Cite this

*Hierarchy of probabilistic principal component subspaces for data mining*. 497-506. Paper presented at Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99), Madison, WI, USA, .