### Abstract

We present a multi-layer competitive model to classify unknown data into hierarchical clusters through unsupervised learning. The first layer of the model, which is a Winner-Takes-All type competitive network within N reference vectors, is an extended Learning Vector Quantization whose distance measure is given by discriminant function of the Bayes decision. The extended LVQ is referred to Learning Baysian Quantization (LBQ). LBQ organizes reference vectors which correspond to prototypes of input data through unsupervised learning. The second layer of the model includes N LBQs as its subnetworks. The a-th reference vector in the first layer connects to the a-th LBQ in the second layer. The a-th LBQ works as a secondary LBQ if and only if the a-th neuron excites. When input data consists of several clusters which include several sub-clusters, the presented model organizes the prototypes of the clusters as the reference vector in the first layer and those of the subclusters as the reference vector in the second layer hierarchically.

Original language | English |
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Pages | 815-818 |

Number of pages | 4 |

Publication status | Published - 1997 Dec 1 |

Event | Proceedings of the 1997 6th IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. Part 1 (of 2) - Victoria, Can Duration: 1997 Aug 20 → 1997 Aug 22 |

### Other

Other | Proceedings of the 1997 6th IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. Part 1 (of 2) |
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City | Victoria, Can |

Period | 97/8/20 → 97/8/22 |

### ASJC Scopus subject areas

- Signal Processing
- Computer Networks and Communications

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## Cite this

*Competitive model to classify unknown data into hierarchical clusters through unsupervised learning*. 815-818. Paper presented at Proceedings of the 1997 6th IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. Part 1 (of 2), Victoria, Can, .