Question

The K in the K-Means algorithm specifies which of the following:

a. The number of partitionuclusters) that we want to get out of a given dataset.
b. This is the maximum number of itentions that the algorithm runs for.
c. This is the number of data-poims that the simitarity metric considen at each iteration.
d. The average distance between clester centroids over all algorithm iterations.

Answer 1

The K in K-Means algorithm specifies the number of clusters in the dataset. Choosing the appropriate measure of center is dependent on the shape of the data. Mean, median, and mode are all different measures of center to consider.

Step-by-step explanation:

The K in the K-Means algorithm specifies the number of clusters that we want to form from a given dataset. This is the primary parameter we set before running the algorithm to partition the dataset into K distinct groups based on similarities among the data points. It does not refer to the number of iterations the algorithm runs for, nor does it indicate the number of data points considered at each iteration or the average distance between cluster centroids over all iterations.

Additionally, when examining datasets, it's critical to determine the most appropriate measure of center. The mean, median, and mode are all measures of center, and the choice between them should be based on the shape of the data. For symmetric distributions, the mean is typically preferred, while the median is better for skewed distributions, and the mode is used when data have one or more high frequency values, which defines a bimodal or multimodal set.