Data Mining and Data Warehousing

Data Mining and Data Warehousing

Pre-Requisite: Database Management Systems

COURSE EDUCATIONAL OBJECTIVES (CEOs): Students will be enabled to understand and implement classical models and algorithms in data warehousing and data mining. They will learn how to analyze the data, identify the problems, and choose the relevant models and algorithms to apply.

COURSE OUTCOMES (COs): At the end of the course, students are able to

CO 1	Understand the basic concepts of data warehouse & data mining.
CO 2	Apply data pre-processing, generalization and data characterization techniques to provide suitable input for a range of data mining algorithms.
CO 3	Analyze and provide solutions for real world problems using mining association techniques.
CO 4	Examine the different classification & clustering techniques in data mining.
CO 5	Apply data mining techniques to complex data objects like spatial data, multimedia data and web mining.

TEXT BOOKS:

T1	Jiawei Han, Micheline Kamber,Data Mining Concepts and Techniques, 2/e, 2006 , Elsevier Publisher (I to V Units).
T2	GK Gupta , Introduction to Data Mining with Case Studies, 2/e, Prentice Hall of India Pvt Limited 2006 (V Unit-Web Mining)

REFERENCE BOOKS:

R1	Pang-Ning tan, Michael Steinbach, Vipin kumar, Introduction to Data Mining, Addision Wesley.
R2	Margaret H. Dunham, Data Mining Introductory and advanced topics, Pearson Education
R3	Arun K Pujari, Data Mining Techniques, University Press.
R4	https://www-users.cs.umn.edu/~kumar001/dmbook/index.php
R5	https://onlinecourses.nptel.ac.in/noc18_cs14/preview

UNIT – I

Data Mining: Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Major issues in Data Mining.

Data Warehouse : Data Warehouse and OLAP Technology for Data Mining Data Warehouse, Multidimensional Data Model, Data Warehouse Architecture, Data Warehouse Implementation, Further Development of Data Cube Technology, From Data Warehousing to Data Mining.

UNIT – II

Data Pre-processing: Needs Pre-processing the Data, Data Cleaning, Data Integration and Transformation, Data Reduction, Discretization and Concept Hierarchy Generation. Data Mining Primitives, Data Mining Query Languages, Concepts Description,

Data Mining Primitives, Languages, And System Architectures: Characterization and Comparison: Data Generalization and Summarization based Characterization, Analytical Characterization, Mining Class Comparisons: Discriminating between Different Classes, Mining Descriptive Statistical Measures in Large Databases.

UNIT – III

Association Rule Mining: Frequent patterns, Apriori algorithm, and FP Growth algorithm. Mining Single-Dimensional Boolean Association Rules from Transactional Databases, Mining Multilevel Association Rules from transaction Databases, Mining Multidimensional Association Rules from Relational Databases, From Association Mining to Correlation Analysis, ConstraintBased Association Mining.

UNIT –IV

Classification and Prediction: Issues Regarding Classification and Prediction, Classification by Decision Tree Induction, Bayesian Classification, Support Vector Machines, Classification Based on Concepts from Association Rule Mining, Rule based induction algorithm, Prediction, Classifier Accuracy.

UNIT-V

Cluster Analysis: Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods, Density-Based Methods, Grid-Based Methods, Model-Based Clustering Methods, Outlier Analysis.

Applications and Trends in Data Mining: Overview of Data Mining Applications Web data mining: Introduction, Web terminology and characteristics, Web content mining, Web usage mining, web structure mining.