Question

which of the following is a bad characteristic of a dataset for k-means clustering analysis. a. data points with outliers b. data points with non-convex shapes c. data points with different densities d. all of others.

Answer 1

Option D is the correct answer. All the mentioned options are bad characteristics of a dataset for k-means clustering analysis.

The k-Means clustering algorithm fails to give good results when the data contains outliers, the density spread of data points across the data space is different and the data points follow non-convex shapes.

The disadvantages of clustering are complexity and the inability to recover from database corruption and trouble clustering data where clusters are of varying sizes and densities.

There are some bad characteristics of k-means clustering analysis:

Being dependent on initial values, Clustering data of varying sizes and densities, and Clustering outliers, Scaling with a number of dimensions.

To learn more about k-means clustering analysis:

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