7.6 kmeans train


You can supply your own dataset as a csv file with a column for each variable, with at least one column. , a row for each instance, and at least your chosen K number of rows. The data is assumed to be numeric and to have been normalised (e.g, converted to numbers in the range -1 to 1) so that no variable dominates any other variable in calculating distances (a distance of 10,000 for incomes of $40,000 and $50,000 swamps a distance of 10 for ages of 40 and 50). See normalise for a helper function to normalise your data.

Common usage:

ml train kmeans 3 iris.csv

Typical output:


Note that the algorithm initialises it’s starting point (the first k centres) randomly, and so the model that is built each time may be quite different.

General usage:

$ ml train kmeans [OPTIONS] K [FILENAME]

     -o <model.csv> --output=<model.csv> Filename of the CSV file to save model, or to STDOUT.
     -m <movie.mpg> --movie=<movie.mpg>  Filename of the movie file to save if desired.
     -v             --view               Popup a movie viewer to visualise the algorithm.
                    --help               Show this message and exit.

The output from train is also a csv file with k rows and a header row. Each of the k rows correspond to a β€œdiscovered” or β€œfit” cluster. This is the trained model. The cluster is represented as the central point of the cluster, calculated as the β€œmean” or β€œaverage” of each of the variables in the dataset across all the points/people in that cluster.

We can use these centroids to place (predict) new observations (people) into one of the k clusters.

The parameter k needs to be provided up frontβ€”i.e., we need to guess a suitable value for the number of clusters. There are other tools that can assist in deciding on a good value for k.

With no csv specified on the command line the csv data (with a header row) is read from standard input. This allows the command to be part of a pipeline of commands, whereby the training data could be piped from another operation.

The default output written to standard output is a csv of the centres, with a cluster label appended to each row, and a header row with the cluster label column named label. Standard output can be redirected to a file or consumed through a pipeline.

If no csv output is specified then the output is always to the terminal, irrespective of whether a mp4 is also output or whether --view is requested.

The output can be saved into a file simply by redirecting standard out (stdout):

ml train kmeans 3 iris.csv > model.csv

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