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25 Νοε 2013 (πριν από 3 χρόνια και 9 μήνες)

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COURSE SYLLABUS


Semester:

Spring 2013

Course Prefix/Number:
CA
P
4770

Course Title:
Data

Mining

Course Credit Hours:
3.0

Course Meeting Time
s
/Place
s
:


Online Campus

Instructor

and Contact Information
:


Dr.
Runa Bhaumik


E
-
mail:
rbhaumik@uwf.edu

Office Hours:

Online

Course
Web Site:

http://elearning.uwf.edu/

(login and select
Data Mining, CAP4770
)

Prerequisites

or Co
-
requisites
:
Database Systems
.

Course Description
:

This course will e
xpose students to data mining
and data warehousing
concepts and techniques
,

and data mining software.
Briefly, the topics that will be covered are:
data preprocessing and
cleaning, association rule mining
, Clustering and Classification
.


Student Learning Outcomes:

Upon successful completion of this course, the student will be able to:

* Identify data mining functionalities

*
Apply data preprocessing techniques
-

data cleaning, data integration and

transformation, data reduction, discret
ization, and concept hierarchy generation

* Mine descriptive statistical measures in large databases

* Mine association rules in large databases

* Learn
classification
algorithms

* Learn Clustering algorithms

* Use data mining software to perform data mi
ning functionalities


mining

association rules, classification and prediction and decision tree analysis.


Required
Text
book(
s
)
:


Data Mining: Concepts and Techniques, by Jiawei Han
,
Micheline Kamber

and Jian Pei
, 2012.
ISBN: 978
-
0
-
12
-
381479
-
1.


2

Reference
s:


Weka’s site:
http://www.cs.waikato.ac.nz/~ml/weka/


About this Course:


This course is delivered completely online. You must have consistent access to the Internet.

Learning at a distance may be a

very different environment for many of you. You will set your
own schedules, participate in class activities at your convenience, and work at your own pace.


You may require some additional time online during the first few
days

while you become
acclimate
d to the online format and you may even feel overwhelmed at times. It will get better.


You should be prepared to spend
more than
8



10
hours per week online completing lessons,
activities, and participating in class discussions. Finally, you may want to
incorporate these tips
to help you get started:




Set a time
at least twice a
week (schedule) to:

o

Check
elearning postings
to determine your tasks.

o

Check
elearning
frequently throughout the week for updates.




Within the first week, become familiar with
ele
arning and

how to use it.

o

It is a tool to help you learn!




Ask questions when you need answers.

o

If you have problems, contact your instructor early.


3

Tentative g
rad
e distribution:

Midterm


15
%

Assignments


40
%

Final
Proje
ct 35
%

Resea
rch paper



10
%

T
echnology
R
equirements:


Knowledge of a machine learning tool


WEKA (on the Windows environment)
will be
necessary for the project
.


Expectations for Academic Conduct/Plagiarism Policy:

Academic Conduct Policy:
(Web Format)

|
(PDF Format)

|
(RTF Format)

Plagiarism Policy:
(Word Format)

|
(PDF Format)

|
(RTF Format)

Student Handbook:
(PDF Fo
rmat)


A
ssistance:

Students with special needs who require specific examination
-
related or other course
-
related
accommodations should contact Barbara Fitzpatrick, Director of Disabled Student Services
(DSS),
dss@uwf.edu
, (850) 474
-
2387. DSS will provide the student with a letter for the
instructor that will specify any recommended accommodations.

Other Course Policies:

Class material

and due dates
:

Students are responsible for all announcements and all material
presented
. Students are expected to keep up with due dates and submit all assignments and work
into the elearning dropbox before the due date.

Communication
:

You are responsible for checking your e
-
mail and the elearning site regularly,
preferably once a day, to ke
ep up with important announcements, assignments, etc.

Exams
: Makeup exams will NOT be given except with a serious, documented medical or legal
excuse. No makeups will be given unless students make advance arrangements. The final exam
is comprehensive.

Re
-
g
rading Assignments
:

It is the student’s responsibility to check graded assignments/tests
when they are returned to you. I will gladly re
-
grade an assignment/test when a question or
mistake is brought to my attention. To ensure fairness, I reserve the right

to re
-
grade the entire
assignment/test. As a result, your grade may increase, decrease, or remain the same. Grades will
not be changed after a week from the date graded assignments/tests are returned to the class.

Grades
:

Final grades will be calculated u
sing a standard grade distribution. The last day of the
term for withdrawal from an individual course with an automatic grade of “W”
is
3/
16
.

Students
requesting
late withdrawal

(W or WF) from class must have the approval of the advisor,
instructor, and th
e department chairperson (in that order) and finally by the Academic Appeals
committee.
Requests for late withdraws may be approved only for the following reasons (which
must be documented):

1. A death in the immediate family.

2. Serious illness of the st
udent or an immediate family member.


4

3. A situation deemed similar to categories 1 and 2 by all in the approval process.

4. Withdrawal due to Military Service (
Florida Statute 1004.07
)

5. National Guard Troops Ordered into Active Service (
Florida Statut
e 250.482
)


Requests without documentation
will

not be accepted.


Requests for late withdraw
al

simply for
not succeeding in a course, do not meet the criteria for approval and
will

not be approved.


Applying for an incomplete or “I” grade will be consider
ed only if: (1) there are extenuating
circumstances to warrant it, AND (2) you have a passing grade and have completed at least 70%
of the course work, AND (3) approval of the department chair.

Participation and Feedback
:

I encourage active participation a
nd regular feedback. I believe
that effective communication between the instructor and students will make the course more
useful, interesting, and productive.
Please contact me if you have any questions, concerns, or
suggestions!


䥭p潲瑡湴t 乯Ne
:
Any chan
ges to the syllabus or schedule made during the semester take
precedence over this version.

Check the elearning site (or email) regularly for up
-
to
-
date
information.

Overall Grading Scale:

1.

A :


93

-

100

2.

A
-
:


9
0


92.999


3.

B+:


87


89.999


4.

B

:

82


86.99
9

5.

B
-

:

79


81.999

6.

C+:


77


78
.999


7.

C


:

72


76
.999


8.

C


:


72
-
76
.999


9.

C
-
:


69
-
71
.999


10.

D+:


67
-
68
.999


11.

D:


59
-
66
.999


12.

F:


0
-
58
.999


Late Policy:

1.

You are expected to complete work on schedule. Deadlines are part of the real
world environment you ar
e being prepared for.

2.

Documentation of health or family problems may be required.

3.

If you have to miss a class, be sure you arrange with another student to find out
what you missed at the earliest possible date.

4.

Late assignments will not be accepted.




There’s another page…keep scrolling down…

5

Tentative
Course
Schedule
:

WEEK #

WEEK OF

TOPIC

PROJECT

1

Jan
7

Chapter 1



Introduction,
What is
data mining, DM
functionalities


Start forming teams
;
Introductions in discussion area


due Friday, Jan 1
1
th
.

2

J
an 1
4

Chapter 1

-

Introduction, What is data mining, DM
functionalities/Find datasets


Team member names
, due
Friday,
Jan

18
th
, 11:00pm.

Ch 1 exercises due,
Friday
,
Jan

18
th
, 11:00pm.

3

Jan
2
1

Chapter
2



Getting to know your data


4

Jan
28

Chapter 2



G
etting to know your data

Chapter 2 exercises due,
Friday
,
Feb 1st
, 11:00pm.

Find

datasets, due
Friday
,
Feb
3
rd
, 1
1:00pm.

5

Feb
4

Chapter
3



Data Preprocessing


6

Feb
11

Chapter
3



Data Preprocessing
,
Tutorial on Weka

Chapter 3 exercises due,
Friday,
Fe
b 1
5
th
, 11:00pm.

7

Feb
18

Chapter 6
-

Mining Frequent Patterns, Associations,
and Correlations

Project Part I


due Friday, Feb
22nd
.

8

Feb
25


Chapter 6

-

Mining Frequent Patterns, Associations,



and Correlations
,

Tutorial on Weka

Association Rule

Chapter 6

exercises due,
Friday
,
March
1st
,
11:00pm.

9

Mar
4

Test 1(M
idterm



on Friday, March
8
th
)



10

Mar
1
1

Chapter 10
-

Clustering: Basic Concepts

Project Part II


due
Mar 1
5
th

10

Mar
1
8

SPRING BREAK


11

Mar
2
5

Chapter 10
-

Clustering: Basic Conce
pts


12

Apr
1

Chapter
1
1



Clustering:
Advanced

Chapter 10 and 11 exercises
due, Friday, Mar
29
t
h
, 11:00pm.

13

Apr
8

Chapter 8 and 9



Classification

.

14

Apr
1
5

Chapter 8 and 9



Classification


15

Apr
2
2

Chapter 8 and 9



Classification

and
Review

C
hapter 8 and 9 exercises due,
Friday, April 2
6
th
.


16

Apr
29

Final Project
,
Wednesday, May 1
st

(comprehensive)

Final DM Project due
,
Wednesday, May 1st.


Important
Note
: Chapter end assignments from the DM book

and
WEKA assignments

will be
assigned

throu
ghout the semester
.
I have posted the due dates of some of the activities, but d
ue
dates for these
and other
assignments
will also be
posted when

the

respective assignments are
assigned.


Also,
the project will be a semester project that will have many par
ts. As the semester
progresses, I will post the dates when each part of the project will be due. Students are expected
to
do
all parts of the project
and all parts of t
he project will receive a grade
(adding to up 30%).


Also, tests and quizzes will be ann
ounced as the semester progresses, so please stay tunned…

And,
enjoy the semester…
and hope you learn a lot about data mining…


Dr
Bhaumik