klustakwik (2.0.1-1) up_extra_files

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+KlustaKwik version 2.01
+----------------------
+
+KlustaKwik is a program for unsupervised classification of multidimensional
+continuous data. It arose from a specific need - automatic sorting of neuronal
+action potential waveforms (see KD Harris et al, Journal of Neurophysiology
+84:401-414,2000), but works for any type of data.  We needed a program that
+would:
+
+1) Fit a mixture of Gaussians with unconstrained covariance matrices
+2) Automatically choose the number of mixture components
+3) Be robust against noise
+4) Reduce the problem of local minima
+5) Run fast on large data sets (up to 100000 points, 48 dimensions)
+
+Speed in particular was essential.  KlustaKwik is based on the CEM algorithm of
+Celeux and Govaert (which is faster than the standard EM algorithm), and also
+uses several tricks to improve execution speed while maintaining good
+performance.  On our data, it runs at least 10 times faster than Autoclass.
+
+Cluster splitting and deletion
+------------------------------
+
+The main improvement in version 1.5 is a cluster splitting feature.  KlustaKwik
+allows for a variable number of clusters to be fit, penalized by AIC. The
+program periodically checks if splitting any cluster would improve the overall
+score.  It also checks to see if deleting any cluster and reallocating its
+points would improve overall score.  The splitting and deletion features allow
+the program to often escape from local minima, reducing sensitivity to the
+initial number of clusters, and reducing the total number of starts needed for
+a data set.
+
+
+Compilation
+-----------
+
+The program is written in C++.  To compile under unix, extract all files to a
+single directory and type make.  That should be all you need to do.  If it
+doesn't work, change the makefile to replace g++ with the name of your C++
+compiler.
+
+To check it compiled properly type "KlustaKwik test 1 -MinClusters 2" to run
+the program on the supplied test file.
+
+Usage
+-----
+
+The program takes a "feature file" as input, and produces two output files, the
+"cluster file", and a log file.  The file formats and conventions may seem
+slightly strange.  This is for historical reasons.  If you want to change the
+code, go ahead, this is open source software.
+
+The feature file should have a name like FILE.fet.n, where FILE is any string,
+and n is a number.  The program is invoked by running "KlustaKwik FILE n", and
+will create a cluster file FILE.clu.n and a log file FILE.klg.n.  The number n
+doesn't serve any purpose other than to let you have several files with the same
+file base.
+
+The first line of the feature file should be the number of input dimensions. 
+The following lines are the data, with each line being one data instance,
+consisting of a list of numbers separated by spaces.  An example file test.fet.1
+is provided.
+
+The first line of the cluster file will be the number of classes that the
+program chose.  The following lines will be the classes asigned to the data
+points.  Class 1 is a "noise cluster" modelled by a uniform distribution, which
+should contain outliers, if there are any.
+
+
+Parameters
+----------
+
+It is possible to pass the program parameters by running "KlustaKwik FILE n
+params" etc.  All parameters have default values. Here are the parameters you can
+use:
+
+-help
+Prints a short message and then the default parameter values.
+
+-MinClusters n   (default 20)
+The random intial assignment will have no less than n clusters.  The final
+number may be different, since clusters can be split or deleted during the
+course of the algorithm
+
+-MaxClusters n   (default 30)
+The random intial assignment will have no more than n clusters. 
+
+-nStarts n       (default 1)
+The algorithm will be started n times for each inital cluster count between
+MinClusters and MaxClusters.
+
+-SplitEvery n    (default 50)
+Test to see if any clusters should be split every n steps. 0 means don't split.
+
+-MaxPossibleClusters n   (default 100)
+Cluster splitting can produce no more than n clusters.
+
+-RandomSeed n    (default 1)
+Specifies a seed for the random number generator
+
+-UseFeatures STRING   (default 11111111111100001)
+Specifies a subset of the input features to use.  STRING should consist of 1s
+and 0s with a 1 indicating to use the feature and a 0 to leave it out.  NB The
+default value for this parameter is 11111111111100001 (because this is what we
+use in the lab) - so if you have more than 12 dimensions you will need to change
+it.
+
+-StartCluFile STRING   (default "")
+Treats the specified cluster file as a "gold standard".  If it can't find a
+better cluster assignment, it will output this.
+
+-DistThresh d    (default 6.907755)
+Time-saving paramter.  If a point has log likelihood more than d worse for a
+given class than for the best class, the log likelihood for that class is not
+recalculated.  This saves an awful lot of time.
+
+-FullStepEvery n (default 10)
+All log-likelihoods are recalculated every n steps (see DistThresh)
+
+-ChangedThresh f (default 0.05)
+All log-likelihoods are recalculated if the fraction of instances changing class
+exeeds f (see DistThresh)
+
+-MaxIter n       (default 500)
+Don't try more than n iterations from any starting point.
+
+-Log             (default 1)
+
+Produces .klg log file (default is yes, to switch off do -Log 0)
+
+-Screen          (default 1)
+
+Produces parameters and progress information on the console. Set to 0 to suppress 
+output in batches.
+
+-Debug           (default 0)
+Miscellaneous debugging information (not recommended)
+
+-DistDump        (default 0)
+Outputs a ridiculous amount of debugging information (definately not recommended).
+
+
+Contact Information
+-------------------
+
+This program is copyright Ken Harris (harris@axon.rutgers.edu), 2000-2002. It
+is distributed under the GNU General Public License (www.gnu.org).  If you make
+any changes or improvements, please let me know.
--- /dev/null
+++ b/test.fet.1
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