NG is formally defined as: G = (W; E), where W is the set of network nodes, E is the set of edges. Nodes denote independent or dependent variables. If two nodes appear in the same sample, an edge is drawn between them. We divide NG nodes into three categories: (a) evaluation factor node representing each evaluation factor, (b) classification node which denotes a grade of one evaluation factor, and (c) output node or clustering center node indicating one grade of output variable. Notice that each output node indicating the clustering center of one community. Each edge is assigned a weight by counting the number of its occurrences in dataset. An example of constructing a NG from the dataset shown in Table 1 is provided as follows.