METHOD FOR IDENTIFYING CLUSTERS OF FLUORESCENCE-ACTIVATED CELL SORTING DATA POINTS

1 .- 5 . (canceled)

6 . An apparatus for creating groupings from data comprising:

means for assigning each piece of data from said set of data to a point on a lattice;

means for assigning weights to each lattice point based on the data near said lattice point; means for determining for each of said lattice points if each of said lattice points should be associated with one of its surrounding lattice points and if so creating a pointer from the individual lattice point to the surrounding lattice point it is associated with; and means for creating clusterings of the lattice points.

7 . A set of application program interfaces embodied on a computer-readable medium for execution on a computer in conjunction with an application program that determines clusters within a set of data, comprising:

a first interface that receives data;

a second interface that receives parameters; and returns groupings of said data.

8 . A method of clustering data items, wherein a data item is associated with one or more semi-continuous values, using an information system comprising:

creating a reduction data item set, each reduction data item associated with one or more quantized values correlated with said one or more semi-continuous values;

assigning each data item to a reduction data item according to an assignment rule; calculating weights for said reduction data items using one or more data items according to a weighting rule;

determining for a plurality of reduction data items if it should be associated with another reduction data item according to an association rule;

for at least one reduction data item, creating a directional association with at least one other reduction data item; and

identifying one or more clusters of said reduction data items using one or more directional associations and/or one or more of said weights.

9 . A method enabling analysis of large sets of data observation points, each point having multiple parameters comprising:

performing a first automated clustering of data points using a subset of said parameters using an information system, said first clustering providing one or more data clusters;

selecting a first selected cluster;

successively performing subsequent automated child clusterings on selected clusters, while optionally choosing different parameters allowing for said clustering.

10 . A method enabling analysis of large sets of data observation points, each point having multiple parameters using an information system comprising:

displaying to a user results of an automated clustering of data points using a subset of said parameters, said first clustering indicating one or more data clusters;

registering an input from said user selecting a first selected cluster from which to generate children clusters;

providing an interface allowing a user to optionally choose different parameters allowing for said children clusters; and

displaying a hierarchy of clustering results.

11 . The method of claim 8 , wherein the step of calculating weights includes linear binning in accordance with the formula:

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12 .- 20 . (canceled)