代写AD699留学生、代做Java/Python设计、代写Business Analytics

日期：2018-11-09 10:13

AD699: Data Mining for Business Analytics

Fall 2018

Homework #4

Due: Monday, 09NOV 11:59 p.m.

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.

This 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 (Association Rules subfolder).

Chapter 14 of our textbook and the slides from Class 7, are also good resources to

have on hand.

You may collaborate with other students, but remember, your artist is unique to

you.

As always, I am available from 1-3 p.m. in FLR-267 on Thursdays, or via e-mail.

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 the first three lines of

your object, write out what they mean. One sentence for each rule is fine. (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. Next, Create an object that includes any three rules for your artist that place your

Artist on the right hand side of the rule. Include a screenshot of your results, along

with the code you used to find this. For the first three lines of your object, write out

what they mean. One sentence for each rule is fine. (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?