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FP5

SIGNAL Report Summary

Project ID: IST-2000-29225
Funded under: FP5-IST
Country: Germany

ROLFs: Regional and online Leanable fields

The ROLFs: Regional and Online Learnable Fields clustering algorithm combines the advantages of the clustering algorithms k-means and k-neighbouring.

In addition the following improvements are realized:
- Representatives are used to reduce memory: The learnt neurons represent the input data. Only the neurons are stored, not the original input data.

- Contiguous areas are detected: Since the clustering algorithm detects contiguous areas within the input space, it is useful to detect partitions, well separated clusters, within the input space.

- Online adaptable: The net learns while presenting pattern by pattern.

- Perceptive fields detect new input patterns: The algorithm is capable to detect patterns belonging to new clusters.

The ROLFs use special artificial neurons that have a perceptive area defined by its position or centre (C) and by its width (sigma). During the learning phase the centre C adapts towards the mean of the input data covered by the perceptive area while
sigma adapts towards the standard deviation.

Adaptation rules known from self-organising maps are transferred to the k-means clustering algorithm to make it online-adaptable. The perceptive area of the neurons serves as a novelty detector for patterns. Thus the net is able to grow and to build representatives to reduce the input data for the epsilon-nearest neighbour.

The ROLFs are a clustering method for unsupervised data clustering, information management and data mining.

Reported by

Division of Neural Computation, University of Bonn
Roemerstr. 164
53117 Bonn
Germany
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