DATA WAREHOUSING AND MINING
PRE-REQUISITE: Basic Programming, Mathematics-Statistics, Database Concepts
This course will introduce the concepts,
techniques, design and applications of data warehousing and data mining. Some
systems for data warehousing and/or data mining will also be introduced. The
course is expected to enable students to understand and implement classical
algorithms in data mining and data warehousing. Students will learn how to
analyze the data, identify the problems, and choose the relevant algorithms to
apply. Then, they will be able to assess the strengths and weaknesses of the
algorithms and analyze their behaviour on real datasets.
|
CO1 |
Understand the concept of Data
Mining, Data Warehouse and Data Marts. |
|
CO2 |
Assess raw input data and
apply data pre-processing techniques, generalization techniques and data
characterization techniques to provide suitable input for a range of data
mining algorithms. |
|
CO3 |
Identify Associations in large
databases using different techniques. |
|
CO4 |
Differentiate various
classification and clustering techniques. |
|
C 5 |
Analyze how data mining
techniques can be applied to complex data objects like spatial data,
multimedia data and web mining. |
|
Course
Code |
COs |
Programme Outcomes |
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|
1 |
2 |
3 |
4 |
5 |
||
|
|
CO1 |
3 |
2 |
1 |
|
|
|
CO2 |
3 |
2 |
|
1 |
|
|
|
CO3 |
3 |
2 |
|
1 |
|
|
|
CO4 |
1 |
2 |
2 |
2 |
|
|
|
CO5 |
|
2 |
3 |
3 |
1 |
|
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1=
Slight(low) 2=Moderate(Medium) 3=Substantial(High) |
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UNIT-I:
Introduction
|
S.No. |
Topics to be
covered |
No. of Classes Required |
Tentative Date of Completion |
Actual Date of Completion |
Teaching Learning Methods |
HOD Sign Weekly |
|
1. |
Introduction
to Course and COs |
1 |
25-11-2019 |
|
TLM1 |
|
|
2. |
Data
mining Functionalities |
1 |
28-11-2019 |
|
TLM1 |
|
|
3. |
Classification
of Data mining systems |
1 |
29-11-2019 |
|
TLM1 |
|
|
4. |
Applications of DM |
1 |
02-12-2019 |
|
TLM1 |
|
|
5. |
Data
mining task primitives |
1 |
05-12-2019 |
|
TLM1 |
|
|
6. |
DM
– on what kind of data |
1 |
06-12-2019 |
|
TLM1 |
|
|
7. |
Multi
dimensional data model, Star, snowflake, galaxy schema |
1 |
09-12-2019 |
|
TLM1 |
|
|
8. |
OLAP AND OLTP, OLAP OPERATIONS Types of OLAP servers |
1 |
12-12-2019 |
|
TLM1 |
|
|
9. |
Data
warehouse Architecture |
1 |
13-12-2019 |
|
TLM1 |
|
|
10. |
TUTORIAL-1 |
1 |
16-12-2019 |
|
TLM3 |
|
|
No.
of classes required to complete UNIT-I: 10 |
No.
of classes taken: |
|||||
UNIT-II:
Data Processing
|
S.No. |
Topics to be covered |
No. of Classes Required |
Tentative Date of Completion |
Actual Date of Completion |
Teaching Learning Methods |
HOD Sign Weekly |
|
1. |
Introduction
to data pre- processing, Data cleaning |
1 |
19-12-2019 |
|
TLM1 |
|
|
2. |
Data
integration |
1 |
20-12-2019 |
|
TLM1 |
|
|
3. |
Data
transformation |
1 |
23-12-2019 |
|
TLM1 |
|
|
4. |
Data
reduction, Dimensionality reduction, Numerosity reduction |
2 |
27-12-2019 |
|
TLM1 |
|
|
5. |
Descretization
and concept hierarchy |
1 |
30-12-2019 |
|
TLM1 |
|
|
6. |
DMQL |
1 |
02-01-2020 |
|
TLM1 |
|
|
7. |
Data generalization and
summarization Analysis of attribute
relevance |
2 |
06-01-2020 |
|
TLM1 |
|
|
8. |
Mining
descriptive statistical measures in large data bases |
1 |
09-01-2020 |
|
TLM1 |
|
|
9. |
TUTORIAL-2 |
1 |
10-01-2010 |
|
TLM3 |
|
|
No.
of classes required to complete UNIT-II: 11 |
No.
of classes taken: |
|||||
UNIT-III:
Association Rule Mining
|
S.No. |
Topics to be
covered |
No. of Classes Required |
Tentative Date of Completion |
Actual Date of Completion |
Teaching Learning Methods |
HOD Sign Weekly |
|
1. |
Association
rule mining introduction
|
1 |
27-01-2020 |
|
TLM1 |
|
|
2. |
Apriori
algorithm, Generating association rules, Methods to Improving apriori |
2 |
30-01-2020 31-01-2020
|
|
TLM1 TLM2 |
|
|
3. |
FP growth
algorithm, Problem using
fp-growth |
2 |
03-02-2020 06-02-2020 |
|
TLM1 |
|
|
4. |
Test |
1 |
07-02-2020 |
|
TLM1 |
|
|
5. |
Mining multi
level assoc rules |
1 |
10-02-2020 |
|
TLM1 |
|
|
6. |
Mining
multi dimensional association rules |
1 |
13-02-2020 |
|
TLM1 |
|
|
7. |
Mining
quantitative association rules |
1 |
14-02-2020 |
|
TLM1 |
|
|
8. |
TUTORIAL-3 |
1 |
17-02-2020 |
|
TLM3 |
|
|
No.
of classes required to complete UNIT-III:10 |
No.
of classes taken: |
|||||
UNIT-IV:
Classification and
Prediction
|
S.No. |
Topics to be
covered |
No. of Classes Required |
Tentative Date of Completion |
Actual Date of Completion |
Teaching Learning Methods |
HOD Sign Weekly |
|
1. |
Classification
and prediction, Issues regarding classification and prediction |
1 |
20-02-2020 |
|
TLM1 |
|
|
2. |
Classification
by decision tree induction, Tree pruning, Extracting rules, Enhancements to
decision tree induction, Scalability and DTI |
3 |
21-02-2020 24-02-2020 27-02-2020 |
|
TLM1 TLM2 |
|
|
3. |
Bayesian
classification Bayes theorem, Naive Bayesian belief networks, Training
Bayesian belief networks |
2 |
28-02-2020 02-03-2020
|
|
TLM1 TLM2 |
|
|
4. |
Linear
regression and non linear regression |
2 |
05-03-2020
06-03-2020
|
|
TLM1 TLM2 |
|
|
5. |
TUTORIAL-4 |
1 |
09-03-2020 |
|
TLM3 |
|
|
No.
of classes required to complete UNIT-IV: 9 |
No.
of classes taken: |
|||||
UNIT-V:
Cluster analysis
|
S.No. |
Topics to be
covered |
No. of Classes Required |
Tentative Date of Completion |
Actual Date of Completion |
Teaching Learning Methods |
HOD Sign Weekly |
|
1. |
Cluster
analysis |
1 |
12-03-2020 |
|
TLM1 |
|
|
2. |
Types of data
in cluster analysis |
1 |
13-03-2020 |
|
TLM2 |
|
|
3. |
Categorization
of clustering methods |
1 |
16-03-2020 |
|
TLM2 |
|
|
4. |
Partitioning
methods k-means, k-mediods, k-mediods to CLARANS, Outlier analysis |
2 |
19-03-2020 20-03-2020 |
|
TLM1 |
|
|
5. |
Text
mining |
1 |
23-03-2020 |
|
TLM1 |
|
|
6. |
Web
mining |
1 |
26-03-2020 |
|
TLM1 |
|
|
7. |
TUTORIAL-5 |
1 |
27-03-2020 |
|
TLM3 |
|
|
No.
of classes required to complete UNIT-V: 08 |
No.
of classes taken: |
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UNIT-I: CLICK HERE
UNIT-II:CLICK HERE
UNIT-III:CLICK HERE
UNIT-IV:CLICK HERE
UNIT-V:CLICK HERE
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