Fuzzy Grouping 
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Fuzzy Grouping
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Fuzzy clustering methods allow samples to belong to more than one group. This easytouse program for the PC allows the user to quickly carry out these techniques and is aimed to meet the needs of ecologists, palaeontology, archaeology and the social sciences. This has not been readily available before, and this has undoubtedly slowed the introduction of these techniques into these fields.
Lotfi Zadeh introduced the notion of a fuzzy set in 1965, as an approach for the handling of uncertain knowledge. Today these techniques are widely used over a range of scientifically disciplines, and seem to be particularly appropriate to ecological analysis where the boundaries between groups such as communities or taxonomic units may be far from sharp. In ordinary cluster analysis a sample or site either belongs, or does not belong, to a particular group or set. We can score membership as a value of 1 and nonmembership as 0. In fuzzy clustering a degree of membership can be assigned as a value between 0 and 1 so that a value of say 0.8 would indicate a high probability that the sample belonged to the particular group in question. Thus, for a data set comprising many samples that can be hypothesized as being divisible into c groups, each sample has a degree of membership of belonging to each of the c groups.
Fuzzy Grouping offers three main methods of data analysis, first, fuzzy cmeans, second, fuzzy ordination and finally fuzzy linear discriminant analysis. The first technique is appropriate for data that you suspect can be divided into cgroups but for which you may have no a priori information on group membership. The second, fuzzy ordination is appropriate when you have information on possible group membership. The second will allow you to plot a selected number of fuzzy groups in a space in which their differences are displayed to best effect and a measure of the ability of the groups to explain the total variability presented.
To help users to understand how to use Fuzzy Grouping, the instructions are written from an ecological viewpoint.
Fuzzy complements CAP (Community Analysis Package) and ECOM (Ecological Community Analysis)
