Advanced Data Mining - Course Handout

BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE, PILANI

WORK INTEGRATED LEARNING PROGRAMMES

Digital

Part A: Content Design

Course Title	Advanced Data Mining
Course No(s)	SS ZG548
Credit Units	4
Credit Model
Content Authors	Kamlesh Tiwari

Course Objectives

No
CO1	To learn how to mine complex data (beyond conventional record data) and complex structures such as Tree/graph, sequence data, web/text data, stream data, mining multivariate time series data, high-dimensional data etc.
CO2	To learn how to apply these techniques to specific applications such as web search, Information Retrieval, social networks etc.
CO3	To learn about distributed computing solutions for data intensive applications in data mining

Text Book(s)

T1
T2

Reference Book(s) & other resources

R1	Tan P. N., Steinbach M & Kumar V. “Introduction to Data Mining” Pearson Education, 2006
R2	Yates R. B. and Neto B. R. “Modern Information Retrieval” Pearson Education, 2005
R3	Han J. & Kamber M., “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publishers, Second Edition, 2006
R4	Christopher D.M., Prabhakar R. & Hinrich S. “Introduction to Information Retrieval” Cambridge UP Online edition, 2009
R5	Hadzic F., Tan H. & Dillon T. S. “Mining data with Complex Structures” Springer, 20
R6	Agarwal Charu C. (Ed) “Data Streams Models and Algorithms” Springer 2007

Content Structure

      1.            Introduction

                        1.1.            Review of data mining

                        1.2.            Objectives

                        1.3.            Overview

      2.            Incremental & Stream Data Mining

                        2.1.            Incremental Algorithms for Data Mining

                        2.2.            Characteristics of Streaming Data

                        2.3.            Issues and Challenges

                        2.4.            Streaming Data Mining Algorithms

      3.            Distributed computing solutions for data mining

                        3.1.            MapReduce/Hadoop

                        3.2.            Spark

      4.            Sequence Mining

                        4.1.            Characteristics of Sequence Data

                        4.2.            Problem Modeling

                        4.3.            Sequence Pattern Discovery

                        4.4.            Timing Constraints

      5.            Text Mining

                        5.1.            Text Classification

                        5.2.            Vector Space Model

                        5.3.            Flat and Hierarchical Clustering

                        5.4.            Streaming Data Mining Algorithms

      6.            Web Search

                        6.1.            Crawling & Indexing

                        6.2.            Hyperlink analysis

                                    6.2.1.                  HITS and Page Rank Algorithms

      7.            Mining Complex Structures

                        7.1.            Mining Trees

                                    7.1.1.                  Tree Miner

                                    7.1.2.                  Tree Model Guided Framework

                                    7.1.3.                  TMG framework for mining ordered & unordered subtrees

                        7.2.            Mining Graphs

                                    7.2.1.                  Approaches to graph mining

                        7.3.            Case Study: Information Retrieval

                        7.4.            Case Study: Mining Social Networks

Learning Outcomes:

No	Learning Outcomes
LO1	To understand how to update the patterns incrementally when the data is continuously coming
LO2	To understand the role of distributed computing in data intensive data mining
LO3	To study how to investigate the sequence data
LO4	To understand how text mining is different from data mining and how to mine it
LO5	To understand what goes into the web search and to study methods of web search and their improvements
LO6	To understand how to mine complex structures other than records while retaining the relations among the entities

Part B: Learning Plan

Academic Term	First  Semester 2017-2018
Course Title	Advanced Data Mining
Course No	SS ZG548
Lead Instructor	Kamlesh Tiwari

Contact Hour 1

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH		Introduction     Review and Overview
During CH
Post CH

Contact Hour 2

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH		Incremental Data Mining Relook traditional algorithms	See Class Slides
During CH
Post CH

Contact Hour 3

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH		Incremental algorithms and their design and analysis	See Class Slides
During CH
Post CH

Contact Hour 4

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH		Incremental algorithms and their design and analysis	See Class Slides
During CH
Post CH

Contact Hour 5

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH		Incremental algorithms and their design and analysis	See Class Slides
During CH
Post CH

Contact Hour 6

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	See Class Slides	Stream Data Mining Characteristics, Issues and Challenges	R6 Ch1,4
During CH
Post CH

Contact Hour 7

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	See Class Slides	Stream Data Mining Algorithms and their Comparison	R6 Ch1, 4
During CH
Post CH

Contact Hour 8

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	See Class Slides	Stream Data Mining Algorithms and their Comparison	R6 Ch1, 4
During CH
Post CH

Contact Hour 9

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	See Class Slides	Distributed computing solutions for data mining	See Class Slides
During CH
Post CH

Contact Hour 10

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	See Class Slides	Distributed computing solutions for data mining	See Class Slides
During CH
Post CH

Contact Hour 11

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	See Class Slides	Distributed computing solutions for data mining	See Class Slides
During CH
Post CH

Contact Hour 12

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	See Class Slides	Distributed computing solutions for data mining	See Class Slides
During CH
Post CH

Contact Hour 13

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R1 7.4	Sequence Mining Characteristics and Problem Modeling	R1 7.4
During CH
Post CH

Contact Hour 14

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R1 7.4	Sequence Pattern Discovery Timing Constraints	R1 7.4
During CH
Post CH

Contact Hour 15

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R4 Ch 1, 13	Text Mining Data Representation and    Characteristics	R4 Ch 1, 13, R2 Ch 7
During CH
Post CH

Contact Hour 16

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R4 Ch 14	Text Classification Feature Selection & Models	R4 Ch 14, R2 Ch 7
During CH
Post CH

Contact Hour 17

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R4 Ch 14	Text Classification Vector Space Model	R4 Ch 14, R2 Ch 7
During CH
Post CH

Contact Hour 18

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R4 Ch 13, 14	Text Classification Multiclass classifiers for text	R4 Ch 13,14
During CH
Post CH

Contact Hour 19

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R4 Ch 16, 17	Text Clustering Flat and hierarchical	R4 Ch 16,17
During CH
Post CH

Contact Hour 20

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R4 Ch 1, 6, 19	Web Search	R4 Ch 1, 6, 19
During CH
Post CH

Contact Hour 21

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R4 Ch 20	Crawling & Indexing	R4 Ch 20
During CH
Post CH

Contact Hour 22

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R4 Ch 20	Crawling & Indexing	R4 Ch 20
During CH
Post CH

Contact Hour 23

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R4 Ch 20	Crawling & Indexing	R4 Ch 20
During CH
Post CH

Contact Hour 24

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R4 Ch 21 See Class slides	Link Analysis	R4 Ch 21
During CH
Post CH

Contact Hour 25

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R5 Ch1 See Class slides	Mining Complex Structures Data Representation	R5 Ch1
During CH
Post CH

Contact Hour 26

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R5 Ch 2, 3 See Class slides	Tree Mining problem and Tree basics	R5 Ch 2, 3
During CH
Post CH

Contact Hour 27

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R5 Ch 3 See Class slides	Tree Miner	R5 Ch 3
During CH
Post CH

Contact Hour 28

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R5 Ch 4, 5, 6	TMG Model Guided Framework	R5 Ch 4, 5, 6
During CH
Post CH

Contact Hour 29

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	R5 Ch 11 See Class slides	Graph Mining Introduction and applications	R5 Ch 11
During CH
Post CH

Contact Hour 30

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	See Class slides	Case Study: Information Retrieval
During CH
Post CH

Contact Hour 31

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	See Class slides	Case Study: Social Network Mining
During CH
Post CH

Contact Hour 32

Type	Content Ref.	Topic Title	Study/HW Resource Reference
Pre CH	See Class slides	Case Study: Social Network Mining
During CH
Post CH

 Evaluation Scheme:  

Legend: EC = Evaluation Component; AN = After Noon Session; FN = Fore Noon Session

No	Name	Type	Duration	Weight	Day, Date, Session, Time
EC-1	Quiz-I/ Assignment-I	Online	-	5%	August 26 to September 4, 2017
	Quiz-II			5%	September 26 to October 4, 2017
	Quiz-III/ Assignment-II			5%	October 20 to 30, 2017
EC-2	Mid-Semester Test	Closed Book	2 hours	35%	24/09/2017 (FN) 10 AM – 12 Noon
EC-3	Comprehensive Exam	Open Book	3 hours	50%	05/11/2017 (FN) 9 AM – 12 Noon

Syllabus for Mid-Semester Test (Closed Book): Topics in Session Nos. 1 to 16 

Syllabus for Comprehensive Exam (Open Book): All topics (Session Nos. 1 to 32)

Important links and information:

Elearn portal: https://elearn.bits-pilani.ac.in

Students are expected to visit the Elearn portal on a regular basis and stay up to date with the latest announcements and deadlines.

Contact sessions: Students should attend the online lectures as per the schedule provided on the Elearn portal.

Evaluation Guidelines:

1.      EC-1 consists of either two Assignments or three Quizzes. Students will attempt them through the course pages on the Elearn portal. Announcements will be made on the portal, in a timely manner.

2.      For Closed Book tests: No books or reference material of any kind will be permitted.

3.      For Open Book exams: Use of books and any printed / written reference material (filed or bound) is permitted. However, loose sheets of paper will not be allowed. Use of calculators is permitted in all exams. Laptops/Mobiles of any kind are not allowed. Exchange of any material is not allowed.

4.      If a student is unable to appear for the Regular Test/Exam due to genuine exigencies, the student should follow the procedure to apply for the Make-Up Test/Exam which will be made available on the Elearn portal. The Make-Up Test/Exam will be conducted only at selected exam centres on the dates to be announced later.

It shall be the responsibility of the individual student to be regular in maintaining the self study schedule as given in the course handout, attend the online lectures, and take all the prescribed evaluation components such as Assignment/Quiz, Mid-Semester Test and Comprehensive Exam according to the evaluation scheme provided in the handout.