Document (#25130)

Author

Polanco, X.
Francois, C.

Title

Data clustering and cluster mapping or visualization in text processing and mining

Source

Dynamism and stability in knowledge organization: Proceedings of the 6th International ISKO-Conference, 10-13 July 2000, Toronto, Canada. Ed.: C. Beghtol et al

Imprint

Würzburg : Ergon

Year

2000

Pages

S.359-365

Series

Advances in knowledge organization; vol.7

Abstract

The focus of this paper is on a cooperative use of the text data clustering and mapping as visualization-based analysis tools. Whether we expose a generic approach in text processing and mining, we only concentrate on the two-middle steps of the process: data clustering and cluster mapping. In the data clustering analysis step, we use the axial k-means (AKM) algorithm: an iterative partitioning unsupervised winner-take-all (WTA) method, producing overlapping clusters. In the step of mapping the clusters, we use a nonlinear multilayer perceptron (MLP) with two hidden layers. Finally, the map is proposed as an analysis device rather than of visualization. It allows the analyst to evaluate the relative position of clusters which are indicators of themes induced from data themselves.

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