代写 html shell graph statistic AD699: Data Mining for Business Analytics Fall 2018

AD699: Data Mining for Business Analytics Fall 2018
Homework #3
Unsupervised Learning Part I​​: ​​Association Rules
For this portion of the assignment, we will be using data from audioscrobbler. ​The data can be found in the nutshell package.​​ The data set is taken from actual data compiled by a music recommender system. Each row in the file represents one customer’s chosen musical artists (i.e. the user listened to songs by those artists).
The link at the end of this paragraph is a very useful document to keep open in your browser as you work on this. ​It includes some template code along with explanations for some of the terminology. http://r-statistics.co/Association-Mining-With-R.html
For some of the functions commonly used with association rules, along with some of the meaning/interpretation for the rules generated using association rules, I highly recommend that you view the “Association Rules & The Sparse Matrix” video in the AD699 Video Library (R Videos Subfolder).
Chapter 14 of our textbook and the class slides that cover Association Rules are also good resources to have on hand.
You ​may collaborate with other students, but remember, your artist is unique to you. To find out who your artist is, please view the file posted along with this assignment.
1. Generate an item frequency barplot for the top 20 artists in this dataset. Include a screenshot of your results, along with the code you used to do this.
2. What is the support for your artist? Do any artists have a higher level of support? If so, which artists? Include a screenshot of your results, along with the code you used to find this.

3.
Create an object that includes ​any​ three rules for your artist that place your artist on the left hand side of the rule. Take a look at this object. Include a screenshot of your results, along with the code you used to find this. ​For any three lines of yourobject,writeoutwhattheymean.​ Besuretoexplainsupport,confidence,
and lift in a way that would make sense to a layperson. (If one of your first three lines includes an empty string for an unknown artist, skip it and go to the next line). In other words, explain them in the way you would explain them to your roommate (I’m assuming your roommate is a smart person who is unfamiliar with data mining).
What meaning might these rules have for audioscrobbler?
​Create an object that includes ​any​ three rules for your artist that place your artist on the right hand side of the rule. Take a look at this object. Include a screenshot of your results, along with the code you used to find this. ​For any three lines of yourobject,writeoutwhattheymean.​ Besuretoexplainsupport,confidence,
and lift in a way that would make sense to a layperson. (If one of your first three lines includes an empty string for an unknown artist, skip it and go to the next line). In other words, explain them in the way you would explain them to your roommate (I’m assuming your roommate is a smart person who is unfamiliar with data mining).
What meaning might these rules have for audioscrobbler?
4.