Around 1,500 people visit an industrial waste treatment facility in central Japan each year to see up close how the operator can recycle more than 99 percent of the solid garbage it receives from a variety of manufacturers and municipalities. Nakadai Co., which covers the Kanto region, accepts 60 tons of waste each day, which it recycles and resells to about 50 customers. The waste includes wooden materials, plastics, cardboard boxes, personal computers, auto parts and fluorescent lamps. Most industrial waste treatment companies specialize in handling a single type of waste for disposal. But Nakadai, founded as a scrap iron processor in Tokyo in 1937, has tried to diversify its sources of income by obtaining most of the nearly 20 types of licenses required for waste disposal since the late 1990s.

The Parameter-Less Self-Organizing Map (PLSOM) is a new neural network algorithm based on the Self-Organizing Map (SOM). It eliminates the need for a learning rate and annealing schemes for learning rate and neighbourhood size. We discuss the relative performance of the PLSOM and the SOM and demonstrate some tasks in which the SOM fails but the PLSOM performs satisfactory. Finally we discuss some example applications of the PLSOM and present a proof of ordering under certain limited conditions.