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Department of Computer Science
Elective Course
Introduction Data warehouse and Data mining
16B1NCI438
Course Coordinator: Avinash Pandey
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Knowledge of Database management system
Introduction
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This course provides an introduction to Data warehouse and data
mining. The course will begin with introduction of Datawarehouse and followed by integration of a data mining system
with data warehouse.
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We shall also do experiments to implement various data mining
techniques such as classification, clustering, etc.
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Introduction to data ware house
 Data warehousing components, data extraction, cleanup, and transformation tools –
metadata; business analysis - reporting and query tools and applications, online
analytical processing (OLAP) – need – multidimensional data model;
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Data Mining
 data mining- introduction – data – types of data – data mining functionalities –
interestingness of patterns – integration of a data mining system with a data
warehouse – issues –data pre-processing;
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Association rule mining and classification
 Mining Frequent Patterns, Associations and Correlations – Mining Methods – Mining
Various Kinds of Association Rules
 Classification and Prediction - Basic Concepts , Decision Tree Induction ,Bayesian
Classification, Support Vector Machines , Other Classification Methods
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Cluster Analysis
 Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods,
Partitioning Methods, Hierarchical Methods, Density-Based Methods, Grid-Based
Methods, Model-Based Clustering Methods, Clustering High-Dimensional Data
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Applications and Trends in Data Mining
 Data Mining Applications: Social Network Analysis , Mining Sequence Patterns in
Biological Data, Text Mining
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Display a comprehensive understanding of different data
mining tasks and the algorithms.
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Apply the techniques of clustering, classification,
association finding, feature selection and visualisation on
real world data.
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Determine whether a real world problem has a data
mining solution.
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Conceptualise a data mining solution to a practical
problem.
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Examination
75 Marks
 T-1
20 Marks
20 Marks
35 Marks
25 Marks
 T-2
 T-3
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Internal assessment
 Attendance
 Project
▪ The class project is an important component of this course. Students will be able to put
their gained knowledge to practice and demonstrates their skills. The project topics should
be approved by the instructor.