Constrained K-means Demonstration
Welcome to the cop-kmeans demo applet! This demo allows you to
specify any number of two-dimensional points and an optional set of
constraints and then cluster the points to see how the constraints
affect the resulting clusters. It uses a modified version of the
k-means clustering algorithm that accommodates any specified pairwise
constraints. See below for instructions.
Warning: If you over-constrain the problem (so that it isn't
possible to find k clusters), nothing happens when you click "Cluster it!".
- Left-click to place points.
- Move the slider to select a value for k (number of clusters).
- [Cluster it!] displays the output for the given number of
clusters. Each point is connected by a green line to
the center of its cluster. In addition, if constraints are
specified, the closure is shown.
- Middle-click and drag to connect two points with a
must-link constraint (blue line).
- Right-click and drag to connect two points with a
cannot-link constraint (red line).
- [Clear all] wipes the data area clean.
- [Clear constraints] removes all constraints but retains the
- [Show closure] displays the transitive closure of the
For more information:
- ... on constrained k-means clustering, see Constrained K-means
Clustering with Background Knowledge (ps, 8 pages, 225k),
by Kiri Wagstaff, Claire Cardie, Seth Rogers, and Stefan Schroedl.
- ... on a modified version of COBWEB that can use constraints,
see Clustering with Instance-level Constraints (ps, 8 pages, 224k), by Kiri Wagstaff and Claire Cardie. ICML 2000.
Kiri Wagstaff < Email : email@example.com >
Last modified: Wed Jun 13 19:36:18 2001