Data Mining Homework 5

plantationscarfAI and Robotics

Nov 25, 2013 (3 years and 9 months ago)

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Data Mining Homework

5


(
47

Points)



1.

(
8

points) Chapter
5
,

exercise 13
a

on page 320.



2.

(
10

points)
Chapter 6 exercise 2a
-
d, on page 404


3.

(
19

points)
A database has 4 transactions, shown below.

TID

Date

items_bought

T100

10/15/04

{K, A, D, B}

T200

10/15
/04

{D, A, C, E, B}

T300

10/19/04

{C, A, B, E}

T400

10/22/04

{B, A, D}


Assuming a minimum level of support
min_sup

= 60% and a minimum level of
confidence
min_conf

= 80%:

(a) Find
all

frequent itemsets (not just the ones with the maximum width./length)

using the Apriori algorithm. Show your work

just showing the final answer is
not acceptable. For each iteration show the candidate and acceptable frequent
itemsets. You should show your work similar to the way the example was done in
the PowerPoint slides
.

(15 points)

(b) List all of the strong association rules, along with their support and confidence
values, which match the following metarule, where
X

is a variable representing
customers and
item
i

denotes variables representing items (e.g., “A”, “B”, etc
.).


x


transaction
,
buys
(X, item
1
)


buys
(X, item
2
)


buys
(X, item
3
)

Hint: don’t worry about the fact that the statement above uses relations. The point
of the metarule is to tell you to only worry about association rules of the form X


Y


Z (or {X, Y
}


Z if you prefer that notation). That is, you
don’t

need to
worry about rules of the form X


Z.

(4 points)


4.

(
10 points
)

Here are several short questions on clustering
.

a.

List one significant commonality between clustering algorithms and
instance based
learning algorithms like nearest
-
neighbor.



b.

A decision tree can be used to generate a partitional clustering. How?



c.

Will outliers have a big impact on the K
-
means algorithm?
Why or why
not?



d.

Will outliers have a big impact on the DB
-
scan algorithm?



e.

We

had talked about Manhattan distances earlier in the course. If this is
used in a clustering algorithm, what shape will the clusters take on?