Hi all,

I am interested in doing a cluster analysis on points in the 2D plane based

on point density (i.e. the number of points per area).

Specifically, given a desired point density D, I want to compute a set of

clusters such that:

1) Each cluster is as big as possible

2) Each cluster contains at least D number of points per unit of area.

As a crude mock-up example involving two clusters, see the illustration in

the following link:

http://www.yqcomputer.com/ ~mdp/cluster_illustration.GIF

There are clearly many variations of cluster analysis around, but I haven't

come across any that will do exactly what I want. For instance I do not want

the number of clusters to be given in advance as in the k-means clustering

method, and I do not necessarily want all points to be included in a cluster

(i.e. the set of clusters need not be a partition of the set of points).

I have been trying to devise a algorithm based on nearest-neighbour

distances and some statistical measures, but I think that an existing (and

much better) solution must be available for this problem. Anyone have any

suggestions?

Any pointers to litterature, online material or other news groups would be

greatly appreciated!

Thank you,

Michael.

Micheal,

I'm not sure if this will help but have you looked at the cluster

analysis methods that are available in the CRIMESTAT program? For more

details and to download it look here -

http://www.yqcomputer.com/

HTH

Greg.

I'm not sure if this will help but have you looked at the cluster

analysis methods that are available in the CRIMESTAT program? For more

details and to download it look here -

http://www.yqcomputer.com/

HTH

Greg.

Greg,

Thank you for the suggestion. CRIMESTAT contains a lot of interesting

algorithms, but the description of these is not readily available, making it

difficult to implement them.

For the record, I was suggested (by Peter Halls) to look into the method

described in the following paper:

Estivill-Castro & Lee (Estivill-Castro, V., & Lee, I., 2002, Argument free

clustering for large spatial point- data sets via boundary extraction from

Delaunay Diagram. CEUS 26 (2002) 315 - 334.).

This seems to be exactly what I want.

/Michael.

Thank you for the suggestion. CRIMESTAT contains a lot of interesting

algorithms, but the description of these is not readily available, making it

difficult to implement them.

For the record, I was suggested (by Peter Halls) to look into the method

described in the following paper:

Estivill-Castro & Lee (Estivill-Castro, V., & Lee, I., 2002, Argument free

clustering for large spatial point- data sets via boundary extraction from

Delaunay Diagram. CEUS 26 (2002) 315 - 334.).

This seems to be exactly what I want.

/Michael.

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