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Data Mining (501445-3)

 Course hashtag #501445DM

Mid-Term Exam Marks

Word Cloud of Data Mining Course, Created by Dr. Alzahrani

Data mining (the advanced analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science,is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. (See Wikipedia)

~Course Information:

    Course syllabus - student version

Code

501445-3

Title

Data Mining

Credit Hrs

3-0:3

Semester

2

Academic Year

2012-2013

Pre/Co Requisites

202225-3 Probability and Statistics, 501347-3 Design And Analysis Of Algorithm

Instructor

Office Hours/ Location

Email

Teaching Assistants

Dr. Salha M. Alzahrani

Mon. 8am-12pm
Tues. 8am-12pm
Garwa deanship block, Room 1630

admin@c2learn.com

s.zahrani@tu.edu.sa

Please send your enquiries to one email address not both!

---

Course Format

Lecture Time/Venue

Course Website

Lectures: 28/Section

Lab: 28/Section

 

www.c2learn.com/data_mining/

~Synopsis:

This course will introduce the students to the basic concepts of data mining and examine methods that have emerged from both fields of statistics and artificial intelligence. The course will survey data mining applications, techniques and models proven to be of value in recognizing patterns and making predictions from a domain perspective. Topics include decision trees, classification, association, partitioning, clustering, and text mining. The course will provide hands-on experimentation of data mining algorithms using easy-to-use software and online repositories.

~Course Objectives:

CO1 : Introduce the basic concepts and techniques of data mining.
CO2 : Develop skills of using recent data mining software for solving practical problems.
CO3 : Gain experience of doing independent study and research.

~Learning Outcomes:

Upon successful completion of the course, the students will be able to:
CLO1 : Describe the basic concepts and techniques of data mining .
CLO2 : Perform the basic data processing in data mining.
CLO3 : Understand the naïve Bayes and nearest neighbour classification algorithms.
CLO4 : Understand and describe the use of decision trees for classification.
CLO5 : Understand and use the association rules for data mining problems.
CLO6 : Understand and differentiate between the hierarchical clustering and k-means clustering.
CLO7 : Understand the basic techniques behind text mining.
CLO8 : Identify appropriate data mining technique in solving a domain’s problem.
CLO9 : Identify the strengths and limitations of popular data mining techniques, and promising business applications of data mining.
CLO10 : Work independently and in a team to write and accomplish a concise research review paper for a selected application of data mining.

~Course Schedule:

Week

Topic

Slides

Assignment(s)/
Lab Exercise(s)

Quiz(zes)

0
26/1/2013
Course syllabus and structure Lecture_0 ---  

1
2/2/2013

Introduction to data mining and knowledge discovery

Lecture_1

 Introduction to WEKA environment

 

2
9/2/2013

Data Processing: Data Cleaning, Preparation, Dealing with Missing Data, and Attributes Reduction

Lecture_2

Introduction to WEKA environment

 

3
16/2/2013

Classification using Naïve Bayes Algorithm

Lecture_3

Discussion on project for writing a concise review paper. Download Instrunctions, IEEE Word template

Group formulation and leader selection.

Confirm selected articles for review.

ARFF files and UCI machine learning repository 

 

4
23/2/2013

Classification using Nearest Neighbour Algorithm

Lecture_4

Format and tips for paper writing.

DM_Handout_Bayes_Classifier

DM_Handout_Nearest_Neighbour

Exercise(s) and practical problems on data processing.

 

5
2/3/2013

Classification using Decision Trees

Lecture_5

Exercise(s) and practical problems on Naïve Bayes classifier.

 

6
9/3/2013

More excercises on Decision Trees

-

DM_Handout_Decision_Trees

Exercise(s) and practical problems on nearest neighbour classifier.

 Quiz I

7

Mid Semester Exams

---

 

 

0

Mid Semester BREAK

 ---

 

 

8

More excercises on Decision Trees  

Exercise(s) and practical problems on classification using decision trees.

DM_Handout_Decision_Trees_Degrees_Dataset

DM_Handout_Decision_Trees_Degrees_Solved

 

9

Inducing Modular Rules for Classification

 

-

Instructions for Writing Your Review Papers.

Samples of Review Papers: Paper-1, Paper-2.

How to use EndNote for citing your references. Download EndNote

More Slides and Tutorials can be found here.

 

9

Inducing Modular Rules for Classification

Lecture_6

DM_Handout_Inducing_Modular_Rules

Exercise(s) and practical problems on classification using decision trees.

 

10

Association Rule Mining: Part I

Lecture_7

DM_Handout_Rule_Interestingness_Measures

Return 1st draft of review paper with comments

Submit 1st draft of review paper.

Exercise(s) and practical problems on association rule mining. 

 

11

Association Rule Mining: Part II

Lecture_8

DM_Handout_Apriori_Algorithm

Exercise(s) and practical problems on clustering. 

12

Clustering: k-means Algorithm

Lecture_9

DM_Handout_kMeans_Clustering_Algorithm

Exercise(s) and practical problems on clustering.   

 

13

Lab Finals and general subjects final exams

---

Return 2nd draft of review paper with comments.

Lab finals. 

 

15&16

Final Exams

---

 

 

Important Downloads:

Academic vocabulary list 
Data mining glossary

~Course Assessment:

Quizzes: 5, Assignment(s)/Project(s): 20, Lab: 15, Mid Term Exam: 20, Final Exam 40.

~Text/References:

Main Textbook:
Bramer, M., Principles of Data Mining. Springer-Verlag. 2007. ISBN 978-1-84628-765-7

Other Reference(s):
Han, J. and Kamber, M., Data Mining: Concepts and Techniques, 2nd Edition, Morgan Kaufmann, 2006.
Cios, K.J., Pedrycz, W., Swiniarski, R.W., Kurgan, L.A., Data Mining: A Knowledge Discovery Approach, Springer US, 2007. 
Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), Morgan Kaufmann, 2005.

~Course Policies:

Attendance

it is compulsory for students to attend at least 75% of the whole time allocated to the course. More than four (4) unjustified absences will make you ineligible for the course.

Quizzes

students are informed about the date of their quizzes. Check the above course schedule to see when a quiz is due.

Assignments

students are expected to submit their assignments in a timely manner.

Late Submissions

late submission may be subjected to a decrease in the grade that is proportional to the delayed days.

Mid-terms and Finals

exams are closed books. Students who did not attend the mid-term due to exceptional situations (e.g., medical emergency) will see their mid-term result calculated from their final. 0.75 * Final exam mark will be used as their mid-term result.

Plagiarism

student caught cheating will be awarded a 0 in the exam and may be subject to university disciplinary actions.

Disability

students with disability need to approach the course instructor so that commodities will be put in place to assist them. This is done in a personal and confidential manner.

 


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