GRAPH LAYOUT USING MACHINE LEARNING TECHNIQUES
interactive graphing systems allow users to rearrange nodes in
a graph that was originally constructed by graph visualization
software using a data file. These graphs can be clustered by characteristics
of the nodes using different algorithms. However, the existing
software allows the user to manipulate the nodes, but does not
interpret the user's input. If the user wants another layout of
the graph, they have to move every node to fit the new layout.
goal of this project is to produce a tool which can interpret
the user's input and rearrange the nodes accordingly. This will
use the k-Means algorithm or a variant of it to cluster
the nodes initially, and then use a machine learning technique
to gauge the user's intent when they move a node and then recompute
the clustering of the nodes using the k-Means algorithm
of this research could include the representations of a library,
a music collection, a class list and many other types of databases.
We will develop test data to determine the usefulness of the product
on different types of data.
project, we will be using graph visualization tools altered to
serve our purpose. Our eventual test will be on data sets up to
a few hundred nodes, however it should be scalable up to or beyond
thousands of nodes.