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NAME: nn_clust.pro PURPOSE: tag events with cluster # CATEGORY: data processing - cluster analysis CALLING SEQUENCE: result = NN_CLUST(data,weights,[N_PARAM=n_param], [N_EVENT=n_event],[N_CLUST=n_clust]) INPUTS: data = data set to be clustered, weights = array of weights which describe cluster centers (produced by the function NN_LEARN) OPTIONAL INPUT PARAMETERS: n_param = # of parameters in data set - 1st dimension of data set n_event = # of events in data set - 2nd dimension of the data set n_clust = # of clusters to find - arbitrary KEYWORD PARAMETERS: none OUTPUTS: result = integer array of cluster assignment of each event OPTIONAL OUTPUT PARAMETERS: none COMMON BLOCKS: none SIDE EFFECTS: none (?) RESTRICTIONS: Read the header of 'nn_learn.pro'. PROCEDURE: The data set is processed against the weights array and a cluster # assignment is made for each event in the input data set based on the weights array which describe the center of each cluster. MODIFICATION HISTORY: initial algorithm: Mark Naiver (Univ of Texas - Austin) Date last modified ==> 1 March 93 : RCH [LANL] Contact: Robb Habbersett (505/667-0296 or robb@big-geek.lanl.gov)
(See /host/bluemoon/usr2/idllib/user_contrib/habbersett/nn_clust.pro)
NAME: nn_learn.pro PURPOSE: Learning step to cluster data using neural network techniques. CATEGORY: Data processing - cluster analysis CALLING SEQUENCE: weights = nn_learn(data,max_val,[BLR=blr],[ELR=elr], [N_EVENT=n_event],[N_PASS=n_pass],[N_PARAM=n_param],[N_CLUST=n_clust] INPUTS: data = data set to be clustered, max_val = maximum range of each parameter in the data set (to normalize the weights). OPTIONAL INPUT PARAMETERS: blr = begining learning rate, elr = ending learning rate n_pass = # of iterations of the learning pass n_param = # of parameters in data set - 1st dimension of data set n_event = # of events in learning set - 2nd dimension of data set n_clust = # of clusters to find - arbitrary (?) KEYWORD PARAMETERS: none OUTPUTS: result = an array of weights describing the cluster centers. OPTIONAL OUTPUT PARAMETERS: none COMMON BLOCKS: none SIDE EFFECTS: This approach has an inherent weakness in that it must be set to find a specific number of clusters; It will find that number of clusters in the data set - regardless. RESTRICTIONS: This routine has not been rigorously tested on different types of data. It "appears" to work on flow cytometry data. PROCEDURE: A limited subset of a larger data set is presented to this routine as a training set to condition the neural network. The result is a set of weights which describe the centers of the resolved clusters. MODIFICATION HISTORY: Initial algorithm: Mark Naiver (Univ of Texas - Austin) Date last modified ==> 1 March 93 : RCH [LANL] Contact: Robb Habbersett (505/667-0296 or robb@big-geek.lanl.gov)
(See /host/bluemoon/usr2/idllib/user_contrib/habbersett/nn_learn.pro)