Answer:
subject, purpose and the recipient details
Explanation:
The email should have a good subject, and that means it should be precise but should convey the purpose and the recipient details. And the detailed report is to be mentioned in the actual body of the email. However, always ensure that the purpose or intent and the recipient details are being mentioned in the subject itself such that the recipient understand in a second the purpose of the email, and also that it meant for him/her.
D: fit to size because it has to fit whatever your putting it on
Answer:
ummm...idr.k..u got me....wat is it
Explanation:
Answer:
10 or 22 or 15 or 40
Explanation:
In the second for loop[for (int k = 1; k < arr[0].length; k++)], k starts from 1 instead of zero. That means that it doesn't read the first value of every row when finding num.
So if num was any of the above values, the program wouldn't work. I had the same question, and the answer for mine was 15.
K-means can be used for hierarchical clustering by creating a hierarchical tree structure. This is done by setting the number of clusters to be created, and then running the k-means clustering algorithm for each level of the tree. For each level, the clusters created are then combined to form the next level of the tree. This process is repeated until the desired number of clusters has been created.
<h3>The Use of K-Means Clustering for Hierarchical Clustering</h3>
K-means clustering is a popular technique used in machine learning and data mining for partitioning data into clusters. It is a flat clustering algorithm, in which data points are grouped according to their similarity. While k-means clustering is suitable for partitioning data into a fixed number of clusters, it can also be used for hierarchical clustering. Hierarchical clustering is a clustering technique that creates a hierarchical tree structure, where each level of the tree is made up of clusters created by the k-means clustering algorithm.
The process of creating a hierarchical tree structure using k-means clustering is fairly straightforward. First, the number of clusters to be created is set, and then the k-means clustering algorithm is run for each level of the tree. For each level, the clusters created are then combined to form the next level of the tree until the desired number of clusters has been created. This process ensures that the clusters created are meaningful and have similar characteristics.
Learn more about k-means :
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