Download CS 432-CS 536-Introduction to Data Mining-Data

Lahore University of Management Sciences
CS 432 – Introduction to Data Mining
CS 536 – Data Mining
Spring 2015-2016
Instructor
Room No.
Office Hours
Email
Telephone
Secretary/TA
TA Office
Hours
Course URL
(if any)
Course Basics
Credit Hours
Lecture(s)
Recitation/Lab
(per week)
Tutorial (per
week)
Mian Muhammad Awais
9-115A
[email protected]
8188
3
Nbr
Per
Nbr
Per
Nbr
Per
of Lec(s)
Week
of Lab(s)
Week
of Lec(s)
Week
2
Duration
0
Duration
Optional
Duration
75 minutes
Course Distribution
Core
Elective
X
Open for Student
Junior, Senior
Category
Close for Student
Freshmen, Sophomore
Category
COURSE DESCRIPTION
Data mining or discovery of knowledge in large datasets has created a lot of interest in
the business and research communities in recent years. The tremendous increase in the
generation and collection of data has highlighted the need for systems that can extract
useful and actionable knowledge from large datasets. This course will provide a
comprehensive introduction to the data mining process; build theoretical and conceptual
foundations of key data mining tasks such as itemset mining and clustering; discuss
analysis and implementation of algorithms; and introduce major sub-areas such as text
and web mining. Emphasis will be placed on the design and application of efficient and
scalable algorithms. The students will get hands on experience through the
implementation of algorithms and use of software in assignments and course project.
COURSE PREREQUISITE(S)

CS 202 - Data Structures, OR grad standing
Lahore University of Management Sciences
COURSE OBJECTIVES



To develop the concepts of and the techniques in key data mining tasks
To provide hands-on experience with data mining using tools
To encourage innovative and useful applications of data mining tasks
Learning Outcomes



Explore, visualize, and analyze large datasets
Select and evaluate data mining techniques for the discovery of relevant
knowledge from datasets
Understand efficiency, scalability, and correctness challenges in data mining
Grading Breakup and Policy
Assignment(s):
10%
Quiz(s):
15%
Midterm Exam: 25%
Project:
15%
Final Exam:
35%
Examination Detail
Midterm
Exam
Yes
Combine Separate:
Duration: 75 minutes
Preferred Date:
Exam Specifications: closed books/notes, help sheet, calculator allowed
Final
Exam
Yes
Combine Separate:
Duration: 2 hours
Exam Specifications: closed books/notes, help sheet, calculator allowed
COURSE OVERVIEW
Lecture
1-2
3-7
Topics
Overview of
Data Mining
Need and motivation; data
mining process; data mining
tasks and functionalities,
interestingness measures
Data Understanding and
Preprocessing
Recommended
Readings
Ch. 1
Ch. 2 & 3
Objectives/
Application
Lahore University of Management Sciences
Data exploration and
visualization; basic stats;
data cleaning, data
reduction, dimensionality
reduction; discretization,
concept hierarchies
8-14
Mining Frequent Patterns and
Associations
15
16-21
Basic definitions, market
basket analysis, Apriori
algorithm, FP-growth
algorithm, mining complex
patterns, constrained
itemset mining, sequential
pattern mining
MIDTERM EXAM
Cluster Analysis
22-27
Similarity measures,
partitioning methods: KMeans, K-Medoids,
hierarchical methods,
density-based methods,
graph-based methods,
outlier/anomaly detection
Applications
28
Sentiment Analysis, opinion
mining, behavior modeling
etc.
Makeup and/or review
Ch. 5; sections from
WDM
Ch. 7, selected papers
Handouts/Relevant Book
Chapters
Textbook(s)/Supplementary Readings
Data Mining: Concepts and Techniques, J. Han, M. Kamber, and J. Pei, Third Edition,
Morgan Kaufmann Publishers, 2011.
Web Data Mining, B. Liu, Springer, 2006.
Introduction to Information Retrieval, C. Manning et al., Cambridge University Press,
Available Online, 2008.
Reference:
Introduction to Data Mining, V. Tan et al. Addison-Wesley, 2006.