PLEASE FOLLOW INSTRUCTION BELOW:
I HAVE ATTACHED THE SYLLABUS TO HELP. BASICALLY FOLLOW THE SYLLABUS TO COME UP WITH THE ANSWER TO THE QUESTION BELOW. NO REFRENCE NEEDED. MY WORKLIFE IS I.T SPECIALIST:
This week is our short week. Please note what you’ve learned in the course and how you will apply it to your work life.
PLEASE FOLLOW INSTRUCTION BELOW:
I HAVE ATTACHED THE SYLLABUS TO HELP. BASICALLY FOLLOW THE SYLLABUS TO COME UP WITH THE ANSWER TO THE QUESTION BELOW. NO REFRENCE NEEDED. MY WORKLIFE IS I.T SPECIALIST:
This week is our short week. Please note what you’ve learned in the course and how you will apply it to your work life.
PLEASE USE TWO REFERENCES AND ANSWER QUESTIONS BELOW:
1.How does data and classifying data impact data mining?
2.What is association in data mining?
3.Select a specific association rule (from the text) and thoroughly explain the key concepts.
4.Discuss cluster analysis concepts.
5.Explain what an anomaly is and how to avoid it.
6.Discuss methods to avoid false discoveries.
PLEASE USE THE BELOW COURSE OBJECTIVES AND OUTCOME AS GUIDE. WRITE A PRACTICAL CONNECTION PAPER THAT REFLECTS ON THE COURSE CONCEPT (DATA SCIENCE) AND HOW YOU CAN APPLY IT TO YOUR JOB AS AN IT PROFESSIONAL.
COURSE OBJECTIVE:
-Explain how businesses can gain competitive advantage through the mining of data,
-Describe when and how various data mining techniques should be applied,
-Describe and explain strategic recommendations based on data mining results.
-Explain the benefits and uses of data through data mining and machine learning lenses and tools.
COURSE OUTCOME:
-Understand the principles of classification and regression techniques
-Know the principles of clustering
-Understand the principles of the association rule
-Understand the principles of spatial and temporal mining
-Understand how to interpret the output produced by classification methods.
THE BOOK REFERENCE USED FOR THE COURSE:
Introduction to Data Mining
9780133128901
Authors: Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar
Publisher: Addison-Wesley
Publication Date: 2013-01-01
Edition: 2nd ED.
https://uk.b-ok.cc/book/3553393/8e28a1
PLEASEUSE ATTACHED PDF AS REFERENCE AND ADD ONE OTHER PEER REVIEW SOURCE. ANSWER THE QUESTIONS BELOW:
1.What are the characteristics of anomaly detection?
2.What are the detection problems and methods?
3.What are the statistical approaches when there is an anomaly found?
4.Compare and contrast proximity and clustering based approaches
PLEASE USE ATTACHED PDF AS REFERENCE AND ADD 1 OTHER PEER REVIEW ARTICLE. ANSWER THE QUESTIONS BELOW:
1.What are the characteristics of data?
2.Compare the difference in each of the following clustering types: prototype-based, density-based, graph-based.
3.What is a scalable clustering algorithm?
4.How do you choose the right algorithm?
