Answer to Question #158787 in Python for shoaib

Question

  The Football Bowl Subdivision (FBS) level of the National Collegiate Athletic Association (NCAA) consists of over 100 schools. Most of these schools belong to one of several conferences, or collections of schools, that compete with each other on a regular basis in collegiate sports. Suppose the NCAA has commissioned a study that will propose the formation of conferences based on the similarities of the constituent schools. Therefore, the director has decided to travel to five schools and the graph is given to you in the following diagram where S is the goal school (state) and P is the initial school (state).

Implement to simulate this problem by Informed Search (A* search).

Here the paths to every school are not directed. The cost of every school’s path is mentioned on the path line in dark and the h(n) values are given on the side of nodes in blue.

For every iteration, you need to display

·        visited school list’s value at present

·        visited school state’s path

·        path length

·        estimated complete [h(n) plus real] path cost

Also provide your comments regarding whether

·        h(n) is acceptable and stable?

·        optimal path is found and why? If not then why not? Also, how would you put the h(n) values of the states to make it right.

Answer 1

within each cluster the following conditions also vary football stadium capacity, latitude, longitude, athletic dept revenue, endowment, and undergraduate enrollment. so we need to get a cluster based on all these variables.

such clustering should be observation clustering. this might be an agglomerative clustering or divisive clustering

we can use any of the following methods of clustering

agglomerative clustering using the linkage methods or k-meas clustering