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Methods for Exploratory Cluster Analysis (2000) [6 citations — 3 self]

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@MISC{Kaski00methodsfor,
    author = {Samuel Kaski and Janne Nikkilä and Teuvo Kohonen},
    title = {Methods for Exploratory Cluster Analysis},
    year = {2000}
}

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Abstract:

When beginning the analysis of a new data set of which very little is known a priori, the first step is to explore the data. This paper presents new methods for this preliminary data mining phase: for detecting, visualizing, and interpreting cluster or density structures of the data. The groundwork for the visualizations is a nonlinear ordered map display constructed using the Self-Organizing Map algorithm. The detected structures can be interpreted using linear local factors extracted from the nonlinear map. In a case study using a collection of patent abstracts the methods have, for instance, detected a cluster of neural networks patents not previously distinguished by the international patent classification system.

Citations

204	Principal curves – HASTIE, W - 1989
105	Neural Computation and Self-Organizing Maps: An Introduction – Ritter, Martinez, et al. - 1992
63	Self-organizing neural networks for visualization and classification – Ultsch - 1992
22	Adaptive Principal Surfaces – LeBlanc, Tibshirani - 1994
4	Teuvo Kohonen. Newsgroup exploration with WEBSOM method and browsing interface – Honkela, Kaski, et al. - 1996
2	Mulier and Vladimir Cherkassky. Self-organization as an iterative kernel smoothing process – Filip - 1995