machine learning algorithms questions

Question

Hi :

I am planing to use k-means to form algorithm to do project. However, I am aware that there are certain shortcomings to find the optimal groups using k-means.

Could you please tell the limitation and provide me with a detailed example?

Thanks

Answer 1

Hello @hideonbush again,

Generally to think about k-means, please refer to below cons and pros. If you can provide more details and how you want to develop your project, I can share more:

Pros:

• K-means is very simple, highly flexible, and efficient.
• Easy to adjust and interpret the clustering results. Easy to explain the results in contrast to Neural Networks.
• The efficiency of k-means implies that the algorithm is good at segmenting a dataset.
• An instance can change cluster (move to another cluster) when the centroids are recomputed

Cons

• It does not allow to develop the most optimal set of clusters and the number of clusters must be decided before the analysis. How many clusters to include is left at the discretion of the researcher. This involves a combination of common sense, domain knowledge, and statistical tools. Too many clusters tell you nothing because of the groups becoming very small and there are too many of them.
• When doing the analysis, the k-means algorithm will randomly select several different places from which to develop clusters. This can be good or bad depending on where the algorithm chooses to begin at. From there, the center of the clusters is recalculated until an adequate "center'' is found for the number of clusters requested.
• The order of the data input has an impact on the final results.

Hope this helps!

Regards,
Yutong

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