visualizations are shown: the color code, with clustering information and the number of hits in each unit, the projection and the labels. Blue, green and red, orange correspond to the top and bottom of Umatrix of map respectively and thus extracted two clusters information. Where p1 (blue color) has been extracted with two hits and in reality p2 is similar to p1. The part p3 (red color) is extracted with 6 hits and p4, p6, p7, p8, p9 are similar to p3, p5 (orange color) and p10 (green color) are extracted with a single hit.

## 4.5 Scatter Plots And Histograms

The figure 3 shows the scatter plots and histograms of all variables of machine-part incidence matrix. Original data points are in the upper triangle, map prototype values on the lower triangle, and histograms on the diagonal: black for the data set and red for the map prototype values. The color coding of the parts has been copied from the map (from the BMU of each part). The variable values have been denormalized.

## 4.6 Machine-Part Cell Formation

The computational result of the visual decipherable clustering of the machine-part cell formation using our SOM approach for the machine-part incidence matrix problem\#1 of table1 are discussed here. The first step in the analysis of the map is visual inspection. 

Here is the U-matrix, component planes and labels in figure 1.

From this first visualization, we can see that: - there are essentially two clusters
- m1,m3,m5,m9,m10 are highly correlated
-m2,m4,m6,m7,m8 are also highly correlated
- one cluster corresponds to the p1,p2,p10 and exhibits
-the other cluster corresponds to p3, p4, p5, p6, p7, p8, p9 Next, the projection of the data set is investigated in figure 2. A principle component projection is made for the data, and applied to the map. The colormap is done by spreading a colormap on the projection. Distance matrix information is extracted from the U-matrix, and it is modified by knowledge of zero-hits (interpolative) units. Finally, three visualizations are shown: the color code, with clustering information and the number of hits in each unit, the projection and the labels. From these figures we can see that: 
 - the projection confirms the existence of two different clusters - interpolative units seem to divide the part family into two classes 8