2. Use “k-Means” operators on the data set. Set k value to 4 and do 6 maximum runs.
3. Report how many items fell into each of the four cluster and the centroid table.
4. Report the cluster with the highest average of weight and cholesterol. What are the average
and range of weight and cholesterol of this cluster?
5. Use “filter examples” operators to get all people in this cluster.
done
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Wk 5 – Signature Assignment: Devil’s Canyon, Part 2 [due Mon] Wk 5 – Signature Assignment: Devil’s Canyon, Part 2
Refer to Devil’s Canyon, Part A in Wk 4.
Using the potential risks for Devil’s Canyon you identified in Wk 4, create a 3- to 4-page matrix to share with the team. In your matrix, you should:
Describe briefly each of the identified risks/threats.
Evaluate the security controls that mitigate each of the risks/threats identified.
Provide a rationale for how each of the controls identified mitigates the risk to an acceptable level.
Research and describe the security technologies and security design that can be used to mitigate each of the identified information security risks.
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Wk 5 Discussion – Information Security Risks
Information Technology Assignment Help
Post a total of 3 substantive responses over 2 separate days for full participation. This includes your initial post and 2 replies to classmates or your faculty member.
Due Thursday
Respond to the following in a minimum of 175 words:
Discuss the steps that an organization takes in order to manage information security risks and build a risk matrix.
What is involved in each step of this process? Use a specific organization in your response.
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Download Chapter07DataSet_Training.csv and Chapter07DataSet_Scoring.csv, from https://sites.google.com/site/dataminingforthemasses/.
2. For the training data, use “Set Role”
Download Chapter07DataSet_Training.csv and Chapter07DataSet_Scoring.csv, from
https://sites.google.com/site/dataminingforthemasses/.
2. For the training data, use “Set Role” operator to set attribute Prime_Sport as label.
3. Feed the training data to “K-NN” operator. Report the knn model.
4. Use “Apply model” operator to apply the learned model to the training data set. Compute the
accuracy of the model.
5. Use “Apply model” operator to apply the learned model to the scoring data set.
6. Replace the “K-NN” operator with “Naïve Bayes”, and repeat 3) and 4).
done
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