Data Mining (IT-603)
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B.Tech RGPV notes AICTE flexible curricula Bachelor of technology
Course Objectives:
1. To introduce data warehouse and its components
2. To introduce knowledge discovery process, data mining and its functionalities
3. To develop understanding of various algorithms for association rule mining and their
differences
4. To introduce various classification techniques
5. To introduce various clustering algorithms.
Syllabus
UNIT 1:
Data Warehousing: Need for data warehousing , Basic elements of data warehousing, Data
Mart, Data Warehouse Architecture, extract and load Process, Clean and Transform data,
Star ,Snowflake and Galaxy Schemas for Multidimensional databases, Fact and dimension
data, Partitioning Strategy-Horizontal and Vertical Partitioning, Data Warehouse and
OLAP technology, Multidimensional data models and different OLAP Operations,
OLAPServer: ROLAP, MOLAP, Data Warehouse implementation, Efficient
Computation of Data Cubes, Processing of OLAP queries, Indexing data.
UNIT 2:
Data Mining: Data Preprocessing, Data Integration and Transformation, Data Reduction,
Discretizaion and Concept Hierarchy Generation, Basics of data mining, Data mining
techniques, KDP (Knowledge Discovery Process), Application and Challenges of Data
Mining
UNIT 3:
Mining Association Rules in Large Databases: Association Rule Mining, SingleDimensional Boolean Association Rules, Multi-Level Association Rule, Apriori Algorithm,
Fp- Growth Algorithm, Time series mining association rules, latest trends in association
rules mining.
UNIT 4:
Classification and Clustering: Distance Measures, Types of Clustering Algorithms, K-Means
Algorithm, Decision Tree, Bayesian Classification, Other Classification Methods,
Prediction, Classifier Accuracy, Categorization of methods, Outlier Analysis.
UNIT 5:
Introduction of Web Mining and its types, Spatial Mining, Temporal Mining, Text Mining,
Security Issue, Privacy Issue, Ethical Issue.
NOTES
- Unit 1
- Unit 2
- Unit 3
- Unit 4
- Unit 5
Books Recommended
1. Arun k Pujari “Data Mining Technique” University Press
2. Han,Kamber, “Data Mining Concepts & Techniques”,
3. M.Kaufman., P.Ponnian, “Data Warehousing Fundamentals”, JohnWiley.
4, M.H.Dunham, “Data Mining Introductory & Advanced Topics”, PearsonEducation.
5. Ralph Kimball, “The Data Warehouse Lifecycle Tool Kit”, JohnWiley.
6. E.G. Mallach , “The Decision Support & Data Warehouse Systems”, TMH
Course Outcomes:
Upon completion of this course, students will be able to
1. Demonstrate an understanding of the importance of data warehousing and OLAP
technology
2. Organize and Prepare the data needed for data mining using pre preprocessing techniques
3. Implement the appropriate data mining methods like classification, clustering or Frequent
Pattern mining on various data sets.
4. Define and apply metrics to measure the performance of various data mining algorithms.
5. Demonstrate an understanding of data mining on various types of data like web data and
spatial data
