Compute the euclidean distance between the two data points

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Reference no: EM132357235

If someone could help me with these questions I would appreciate it.

1. Typically, K-means algorithm need multiple iterations to generate desirable results. Under what condition, the K-means algorithm will coverage or end? Choose all that apply.

a. clustering yields desirable number of clusters

b. No data points need to change their cluster

c. No centroids need to move their location

d. All data points have their own cluster

e. All clusters have sufficient data points

2.Which of the following is true of data mining (DM) process?

a. As long as a DM model is mathematically approved, it should be a useful one

b. The first step in a DM process is data understanding, which is crucial to a successful DM outcome but often overlooked

c. As long as we follow the CRISP_DM correctly, desirable DM results are guaranteed in one cycle, without any iteractions

d. In the stage of data preparation, you need to gather your data assets, reduce datasets, clean your data, and reformat your data

e. In order to deploy a DM model successfully, you need to clearly communicate the model's function and utility to stakeholders, test and prove the model, and plan for and monitor the implementation.

3. Please compute the Euclidean distance between the two data points: A(4,7), and B (4,10).

4. Please compute the Manhattan distance between the two data points: A(1,16), and B (7,8).

Reference no: EM132357235

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