BIRLA
INSTITUTE OF TECHNOLOGY & SCIENCE, PILANI
WORK
INTEGRATED LEARNING PROGRAMMES
Part A:
Content Design
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Course Title
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Advanced
Data Mining
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Course No(s)
|
SS
ZG548
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Credit Units
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4
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Credit Model
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|
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Content Authors
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Dr.
Kamlesh Tiwari
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Course Objectives
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No
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CO1
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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.
|
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CO2
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To learn how to apply these techniques to specific applications
such as web search, Information Retrieval, social networks etc.
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CO3
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To learn about distributed computing solutions for data
intensive applications in data mining
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Text
Book(s)
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T1
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T2
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Reference
Book(s) & Other resources
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R1
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Tan P. N.,
Steinbach M & Kumar V. “Introduction
to Data Mining” Pearson Education, 2006
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R2
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Yates R. B. and Neto B. R. “Modern
Information Retrieval” Pearson Education, 2005
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R3
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Han J. & Kamber M., “Data
Mining: Concepts and Techniques”, Morgan Kaufmann Publishers, Second
Edition, 2006
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R4
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Christopher D.M., Prabhakar R. &
Hinrich S. “Introduction to Information Retrieval” Cambridge UP Online
edition, 2009
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R5
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Hadzic F., Tan H. & Dillon T. S. “Mining
data with Complex Structures” Springer, 20
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R6
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Agarwal Charu C. (Ed) “Data Streams
Models and Algorithms” Springer 2007
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R7
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R8
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R9
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Azure Cosmos DB https://docs.microsoft.com/en-us/azure/cosmos-db/introduction
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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
2.5.
Executing Streaming Data Mining on
HDInsight/Data Science VM
3.
Distributed computing solutions for data
mining
3.1.
MapReduce/Hadoop
3.2.
Spark
3.3.
Setting up Hadoop and Spark cluster on
Azure HDInsight to perform Data mining tasks
4.
Sequence Mining
4.1.
Characteristics of Sequence Data
4.2.
Problem Modeling
4.3.
Sequence Pattern Discovery
4.4.
Timing Constraints
4.5.
Performing Data Mining Algorithm on
Azure Data Science VM
5.
Text Mining
5.1.
Text Classification
5.2.
Vector Space Model
5.3.
Flat and Hierarchical Clustering
5.4.
Streaming Data Mining Algorithms
5.5.
Text Classification on HDInsight
6.
Web Search
6.1.
Crawling & Indexing
6.2.
Hyperlink analysis
6.3.
HITS and Page Rank Algorithms
6.3 Building Web Search using built-in library
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
7.2.2.
Building and Traverse Graph Database
using Azure Cosmos DB
7.3.
Case Study: Information Retrieval
7.4.
Case Study: Mining Social Networks
Learning
Outcomes:
|
No
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Learning Outcomes
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LO1
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To understand how to update the
patterns incrementally when the data is continuously coming
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LO2
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To understand the role of distributed
computing in data intensive data mining
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LO3
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To study how to investigate the
sequence data
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LO4
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To understand how text mining is
different from data mining and how to mine it
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LO5
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To understand what goes into the web
search and to study methods of web search and their improvements
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LO6
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To understand how to mine complex
structures other than records while retaining the relations among the
entities
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LO7
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Familiarity with tool used for Data
mining and advance analytics on azure
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Part
B: Learning Plan
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Academic Term
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Second Semester 2017-2018
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|
Course Title
|
Advanced
Data Mining
|
|
Course No
|
SS
ZG548
|
|
Lead Instructor
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Dr.
Kamlesh Tiwari
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Contact Hour 1
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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Introduction
Review and Overview
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During CH
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|||
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Post CH
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Contact Hour 2
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Type
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Content Ref.
|
Topic Title
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Study/HW Resource Reference
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Pre CH
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Incremental Data Mining
Relook traditional algorithms
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See Class Slides
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During CH
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|||
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Post CH
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Contact Hour 3
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Type
|
Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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Incremental algorithms and their
design and analysis
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See Class Slides
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During CH
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|||
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Post CH
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Contact
Hour 4
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Type
|
Content
Ref.
|
Topic
Title
|
Study/HW
Resource Reference
|
|
Pre
CH
|
|
Incremental
algorithms and their design and analysis
|
See
Class Slides
|
|
During
CH
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|||
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Post
CH
|
Contact
Hour 5
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Type
|
Content
Ref.
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Topic
Title
|
Study/HW
Resource Reference
|
|
Pre
CH
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|
Incremental
algorithms and their design and analysis
|
See
Class Slides
|
|
During
CH
|
|||
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Post
CH
|
Contact Hour 6
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Type
|
Content Ref.
|
Topic Title
|
Study/HW Resource Reference
|
|
Pre CH
|
See Class Slides
|
Stream Data Mining Characteristics,
Issues and Challenges
|
R6 Ch1,4
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During CH
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|||
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Post CH
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Contact Hour 7
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Type
|
Content Ref.
|
Topic Title
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Study/HW Resource Reference
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Pre CH
|
See Class Slides
|
Stream Data Mining Algorithms and
their Comparison
|
R6 Ch1, 4
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During CH
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|||
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Post CH
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Contact Hour 8
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Type
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Content Ref.
|
Topic Title
|
Study/HW Resource Reference
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|
Pre CH
|
See Class Slides
|
Stream Data Mining Algorithms and
their Comparison
|
R6 Ch1, 4
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During CH
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|||
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Post CH
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Contact Hour 9
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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
|
|||
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Post CH
|
Contact Hour 10
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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
|
|||
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Post CH
|
Contact Hour 13
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Type
|
Content Ref.
|
Topic Title
|
Study/HW Resource Reference
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Pre CH
|
R1 7.4
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Sequence Mining
Characteristics and Problem
Modeling
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R1 7.4
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During CH
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|||
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Post CH
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Contact Hour 14
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Type
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Content Ref.
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Topic Title
|
Study/HW Resource Reference
|
|
Pre CH
|
R1
7.4
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Sequence Pattern Discovery
Timing Constraints
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R1 7.4
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During CH
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|||
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Post CH
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Contact Hour 15
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Type
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Content Ref.
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Topic Title
|
Study/HW Resource Reference
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Pre CH
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R4 Ch 1, 13
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Text Mining
Data Representation and Characteristics
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R4 Ch 1, 13, R2 Ch 7
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During CH
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|||
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Post CH
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Contact Hour 16
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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|
Pre CH
|
R4 Ch 14
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Text
Classification
Feature
Selection & Models
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R4 Ch 14, R2 Ch 7
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During CH
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|||
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Post CH
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Contact Hour 17
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Type
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Content Ref.
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Topic Title
|
Study/HW Resource Reference
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Pre CH
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R4 Ch 14
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Text Classification
Vector Space Model
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R4 Ch 14, R2 Ch 7
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During CH
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|||
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Post CH
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Contact Hour 18
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Type
|
Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
|
R4 Ch 13, 14
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Text
Classification
Multiclass
classifiers for text
|
R4 Ch 13,14
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|
During CH
|
|||
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Post CH
|
Contact Hour 19
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Type
|
Content
Ref.
|
Topic
Title
|
Study/HW
Resource Reference
|
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Pre
CH
|
R4
Ch 16, 17
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Text
Clustering
Flat
and hierarchical
|
R4
Ch 16,17
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During
CH
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|||
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Post
CH
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Contact Hour 20
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Type
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Content
Ref.
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Topic
Title
|
Study/HW
Resource Reference
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Pre
CH
|
R4
Ch 1, 6, 19
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Web
Search
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R4
Ch 1, 6, 19
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During
CH
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|||
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Post
CH
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Contact Hour 21
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Type
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Content Ref.
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Topic Title
|
Study/HW Resource Reference
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Pre CH
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R4 Ch 20
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Crawling & Indexing
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R4 Ch 20
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During CH
|
|||
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Post CH
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Contact Hour 22
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Type
|
Content
Ref.
|
Topic
Title
|
Study/HW
Resource Reference
|
|
Pre
CH
|
R4
Ch 20
|
Crawling
& Indexing
|
R4
Ch 20
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During
CH
|
|||
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Post
CH
|
Contact Hour 23
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Type
|
Content Ref.
|
Topic Title
|
Study/HW Resource Reference
|
|
Pre CH
|
R4 Ch 20
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Crawling & Indexing
|
R4 Ch 20
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During CH
|
|||
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Post CH
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Contact Hour 24
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Type
|
Content
Ref.
|
Topic
Title
|
Study/HW
Resource Reference
|
|
Pre
CH
|
R4
Ch 21
See
Class slides
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Link
Analysis
|
R4
Ch 21
|
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During
CH
|
|||
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Post
CH
|
Contact Hour 25
|
Type
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Content Ref.
|
Topic Title
|
Study/HW Resource Reference
|
|
Pre CH
|
R5 Ch1
See Class slides
|
Mining Complex Structures
Data Representation
|
R5 Ch1
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During CH
|
|||
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Post CH
|
Contact Hour 26
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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
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During CH
|
|||
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Post CH
|
Contact Hour 27
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Type
|
Content Ref.
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Topic Title
|
Study/HW Resource Reference
|
|
Pre CH
|
R5 Ch 3
See Class slides
|
Tree Miner
|
R5 Ch 3
|
|
During CH
|
|||
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Post CH
|
Contact Hour 28
|
Type
|
Content Ref.
|
Topic Title
|
Study/HW Resource Reference
|
|
Pre CH
|
R5 Ch 4, 5, 6
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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
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|
|
During CH
|
|||
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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%
|
February
1 to 10, 2018
|
|
|
Quiz-II
|
|
|
5%
|
March
1 to 10, 2018
|
|
|
Quiz-III/ Assignment-II
|
|
|
5%
|
March
20 to 30, 2018
|
|
EC-2
|
Mid-Semester Test
|
Closed Book
|
2 hours
|
35%
|
04/03/2018 (FN) 10 AM – 12 Noon
|
|
EC-3
|
Comprehensive Exam
|
Open Book
|
3 hours
|
50%
|
22/04/2018 (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.
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