Self-organizing maps: fuzzy input?

I would like to know if there are SOM implementations (preferably R) available that accept fuzzy input. That is, I have data in which some nominal features are spread out between a number of categories. For example: feature 1 has 5 categories and an observation might have the values (which are actually probabilities) [0, 0.5, 0.25, 0.25, 0].

• Why wouldn't you just treat the 5 categories as 5 separate features? Dec 2, 2013 at 6:00
• This does sound as a good and simple option, thank you. Is this good/best practice? I suppose there is no real way of avoiding the increased number of inputs? I ask because my sample size is quite small. Dec 2, 2013 at 8:42

Janos Abonyi, Sandor Migaly and Ferenc Szeifer, "Fuzzy Self-Organizing Map based on Regularized Fuzzy $c$-means Clustering"
This paper presents a new fuzzy clustering algorithm for the clustering and visualization of high-dimensional data. The cluster centers are arranged on a grid defined on a small dimensional space that can be easily visualized. The smoothness of this mapping is achieved by adding a regularization term to the fuzzy $c$-means (FCM) functional. The measure of the smoothness is expressed as the sum of the second order partial derivatives of the cluster centers. Coding the values of the cluster centers with colors, regions with different colors evolve on the map and the hidden relation between the variables reveal. Comparison to the existing modifications of the fuzzy $c$-means algorithm and several application examples are given.