Unsupervised Clustering


Unsupervised Clustering

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The unsupervised clustering function uses k-means on perceptually relevant dimensions of the extracted contour, to place calls into a predefined number of clusters.

Each call is segmented into six partitions. The k-means algorithm operates on the slope and frequency of each partition, as well as the sinuosity of the first and second half of the call, and the call duration.

To perform unsupervised clustering using k-means:

  1. Click "Tools > Call Classification > Unsupervised Clustering"
  2. Select the detection files to cluster OR select the saved contours.
  3. After the detection files are processed, you may save the extracted contours for faster loading.
  4. Choose the clustering method. ARTwarp is still experimental, so k-means is currently recommended.
  5. Enter the weights (relative importance) of each dimension.
  6. When asked whether to use an existing model, click "No".
  7. Enter the number of call categories. Our work-flow involved producing more clusters than desired, and training a supervised neural network on the best clusters.
  8. Once clustering finishes, you will be prompted to save the model. This is optional.
  9. A new interface will appear, showing the clusters. This interface can also be found under "Tools > Call Classification > View Clusters"
    • Name the clusters by entering a name in the text box. Clusters with the same name will be merged upon saving.
    • View different clusters with the "Next" and "Back" buttons.
    • View more calls within a cluster with the "Next Page" and "Previous Page" buttons.
    • Reject calls by clicking on them. Calls highlighted in red will be rejected upon saving.
    • Update the call files by clicking "Save", or redo the clustering with "Redo"