Community Ecology
Volume/Issue:
Volume 2: Issue 2
Minimum message length clustering: an explication and some applications to vegetation data
Authors:
Mike B. Dale Griffith University Australian School of Environmental Studies Qld. 41112 Nathan

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L. Salmina University of Latvia Department of Botany and Ecology 4 Kronvalda Blvd LV1586 Riga

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, and
L. Mucina University of the North School of Life Sciences Qwa-Qwa Campus, Private Bag X13 9866 Phuthaditjhaba

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Pages:
231–247
Online Publication Date:
22 Jul 2005
Publication Date:
01 Dec 2001
Article Category:
Research Article
DOI:
https://doi.org/10.1556/comec.2.2001.2.11
Keywords:
Fuzzy clustering; Fuzzy clustering; MML principle; Qualitative data; Quantitative data; Snob program
Restricted access

In this paper, we examine the application of a particular approach to induction, the minimum message length principle and illustrate some of the problems that can be addressed through its use. The MML principle seeks to identify an optimal model within some specified parameterised class of models and for this paper we have chosen to concentrate on a single model class, that of mixture separation or fuzzy clustering. The first section presents, in outline, an MML methodology for fuzzy clustering. We then present some applications, including the nature of the within-cluster model, examination of the univocality of results for different groups of species and the effectiveness of presence data compared to purely quantitative data. Finally, we examine some possibilities of extending MML methodology to include within-class correlation of species, the existence of dependence between observed samples and the comparison of different classes of models.

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Community Ecology An Interdisciplinary Journal Reporting Progress in Community and Population Studies
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Community Ecology
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