The data can be binary or continuous and some data have very different values between minimum and maximum. Very high difference of ranges of data values will result to major errors. A good method of scaling should be required.Size of the Map
The visualizing methods also consider the positions of the next best matches. There is no fixed rule to assign the size of the map that might be appropriated with the number of inputs data. A large map however, can reduce the average mismatch of distinct clusters, but it requires a large number of iterations and qualified hardware. For example, to detect structure in the U-Matrix landscape, it is required very large Kohonen maps, normally, a 64 by 64 or 128 by 128 map which are calculated by using a parallel machine with 128 MIMD processor. A good view of U-matrix is visualized on the Silicon Graphics machine.Cluster Boundaries
SOFM cell displays, generally do not produce visual evidence that leads to good guesses about cluster substructure or data density even for 2-dimension data. The distance between the different clusters is large hence the cluster boundaries are very distinct. But a few data cannot be distinguised, hence the boundary are detected from the attribute values. Visualization techniques can be improved to avoid this constrains.Interface with Experts
The knowledge discovery module converts the high-dimension structure of the self-organized neural network into symbolic rules. These rules can be interpreted by an expert of the domain to produce machine learning algorithm for rule extraction.Fuzzy Logic Technique
The neuro-fuzzy is the a main facility required for the next generation intelligence technologies. They combine features of learning and adaptive methods along with prior knowledge in the form of fuzzy rules.
Knowledge acquisition is often a bottleneck in artificial intelligence applications. For human experts it is, however, difficult to formulate their knowledge in the symbolic form. Therefore, Knowledge-based System may not be able to diagnose cases that experts can to do. Although neither systems discovered new clustering technique of methodological significance, we would like to pursue this research.