Final answer:
The statement "The k-means algorithm of clustering minimizes the within variance and maximizes the between variance which means that hazard groups are homogeneous and well separated" is True.
Step-by-step explanation:
The k-means algorithm is a clustering algorithm in mathematics that aims to minimize the within variance and maximize the between variance.
The within variance is a measure of the similarity within each cluster, while the between variance is a measure of the difference between clusters.
By minimizing the within variance, the algorithm ensures that each cluster is homogeneous, meaning that the data points within each cluster are similar to each other.
By maximizing the between variance, the algorithm ensures that the clusters are well separated, meaning that the data points in different clusters are different from each other.
Hence, the correct answer is: True.
The complete question is: The k-means algorithm of clusteringminimizes the within variance and maximizes the between variance which means that hazard groups are homogeneous and well separated. True or False.