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PEIT5302 DATA MINING & DATA WAREHOUSING (3-0-0)
Instructor: Prof. Puspanjali Mohapatra
No. of lectures: 40
Objective of the Course:-After learning data Mining, the students can extract the hidden predictive information
from large databases.
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Data mining, the extraction of hidden predictive information from large
databases, is a powerful new technology with great potential to help companies
focus on the most important information in their data warehouses.
Data mining tools predict future trends and behaviors, allowing businesses to
make proactive, knowledge-driven decisions.
The automated, prospective analyses offered by data mining move beyond the
analyses of past events provided by retrospective tools typical of decision support
systems.
Data mining tools can answer business questions that traditionally were too time
consuming to resolve.
Module - I (12 Hours )
Overview: Data warehousing, The compelling need for data warehousing, the
Building blocks of data warehouse, data warehouses and data marts, overview of
the components, metadata in the data warehouse, trends In data warehousing,
emergence of standards, OLAP, web enabled data warehouse, Introduction to the
data warehouse project, understanding data warehousing Architecture, Data
warehousing implementation, from data warehousing to data mining.
Module – II( 14 Hours)
Introduction to Data mining, Data mining Functionalities, Data preprocessing (data
summarization, data cleaning, data integration and transformation, data reduction,
data discretization),
Mining frequent patterns, associations, correlations (market basket analysis, the
apriori algorithm, mining various kinds of association rules, from association mining
to correlation analysis)
Classification: classification by decision tree induction, Rule based classification,
classification by neural networks, classification by genetic algorithm.
Module - III (10 Hours)
Cluster Analysis: types of data in cluster analysis, A categorization of major
clustering methods (partitioning methods, hierarchical methods),clustering high
dimensional data, outlier analysis
Advanced techniques: web mining, spatial mining, temporal mining, Data mining
applications in (financial data Analysis, retail industry, telecommunication industry,
Biological data analysis, intrusion detection, in other scientific applications)
Text Books:
1. Data warehousing Fundamentals: Paulraj Ponniah, Willey India.
2. Data Mining: Concepts and techniques: J.Han and M.Camber, Elsevier.
Reference books:
1. Data Mining: Arun Pujari, University Press
2. Data Mining –a Tutorial based primer by R.J.Roiger, M.W.Geatz, Pearson
Education.
3. Data Mining & Data Warehousing Using OLAP: Berson, TMH. 4. Data
Warehousing: Reema Thareja, Oxford University Press
PEIT5302 COURSE DETAILS
SL. NO.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
TOPICS
NO OF
HOURS
Syllabus Overview, Introduction to Data Mining and Data
Warehousing,
Overview: Data warehousing, The compelling need for data
warehousing,
The Building blocks of data warehouse, data warehouses and data
marts,
Overview of the components, metadata in the data warehouse
1
Trends In data warehousing,
Emergence of standards, OLAP
Web enabled data warehouse
Introduction to the data warehouse project,
Understanding data warehousing Architecture,
Data warehousing implementation
from data warehousing to data mining.
Summary Discussion of Module-1
Introduction to Data mining,
Data mining Functionalities,
Data preprocessing (data summarization, Data cleaning, data integration
and transformation, Data reduction, data discretization),
Mining frequent patterns,
associations,
correlations (market basket analysis, the apriori algorithm)
mining various kinds of association rules,
from association mining to correlation analysis
Classification: classification by decision tree induction
Rule based classification,
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
1
1
2
2
23
24
25
26
27
28
29
30
31
32
33
34
35
classification by neural networks,
2
classification by genetic algorithm
2
Summary Discussion of Module-2
2
Cluster Analysis: types of data in cluster analysis,
2
A categorization of major clustering methods(partitioning methods, 1
hierarchical methods)
clustering high dimensional data, outlier analysis
1
Advanced techniques: web mining,
1
spatial mining, temporal mining,
1
Data mining applications in financial data Analysis,
1
retail industry, telecommunication industry
1
Biological data analysis
1
intrusion detection, in other scientific applications
1
Summary Discussion of Module-3
1