Lesson Plan

Name: Dr. S Rao Chintalapudi                              Subject: Data Warehousing and Mining Designation: Associate Professor                           Subject Code:  16CS6T16   

Department:CSE                                                      Year & Semester:III B.Tech- II sem

LESSON PLAN

S.No.	Topic	Mode of Lecture	No. of Classes
UNIT-I: Data Warehouse and OLAP Technology			10
1	Introduction to Data Warehouse	Board & Chalk	2
2	A Multidimensional Data Model	PPT	2
3	Data Warehouse Architecture	PPT	2
4	Data Warehouse  Implementation	PPT	2
5	Data Warehousing to Data Mining	PPT	2
UNIT II: Data Mining			10
1	Introduction to Data Mining	Board & Chalk	1
2	Motivating challenges	Board & Chalk	1
3	The origins of Data Mining	Board & Chalk	1
4	Data Mining Tasks	PPT	2
5	Types of Data	Board & Chalk	1
6	Data Quality	Board & Chalk	1
7	Exploring Data	PPT	1
8	The Iris Dataset	PPT	1
9	summary statistics	Board & Chalk	1
UNIT-III: Data Preprocessing			9
1	Introduction to Data Preprocessing	Board & Chalk	1
2	Aggregation, Sampling	Board & Chalk	1
3	Dimensionality Reduction	PPT	2
4	Feature Subset Selection, Feature creation	Board & Chalk	1
5	Discretization and Binarization	Board & Chalk	1
6	Variable Transformation	Board & Chalk	1
7	Measures of Similarity and Dissimilarity	PPT	2
UNIT-IV: Classification & Model Overfitting			12
1	General Approach to solving a classification problem	Board & Chalk	1
2	Working of Decision Tree, building a decision tree	PPT	2
3	Methods for expressing an attribute test conditions	Board & Chalk	1
4	Measures for selecting the best split, Algorithm for decision tree induction	PPT	2
5	Model Overfitting, Due to presence of noise	PPT	1
6	Due to lack of representation samples	PPT	1
7	Evaluating the performance of classifier: holdout method	Board & Chalk	1
8	Random sub sampling method	Board & Chalk	1
9	cross-validation, bootstrap methods	Board & Chalk	1
10	Alternative Techniques: Bayes’ Theorem, Naïve Bayesian Classification	PPT	1
UNIT-V: Association Analysis			11
1	Introduction to Association analysis	Board & Chalk	1
2	Basic Concepts, Problem Definition	Board & Chalk	1
3	Frequent Item Set generation using Apriori	PPT	2
4	Rule generation	Board & Chalk	2
5	compact representation of frequent item sets	PPT	2
6	FP-Growth Algorithm	PPT	3
UNIT-VI: Cluster Analysis			10
1	Introduction to Cluster Analysis	Board & Chalk	1
2	Different Types of Clustering, Different Types of Clusters	PPT	1
3	The Basic K-means Algorithm	PPT	2
4	K-means: Additional Issues, Bisecting K-means	Board & Chalk	1
5	K-Means: Strengths and Weaknesses	Board & Chalk	1
6	Agglomerative Hierarchical Clustering & algorithm	Board & Chalk	1
7	DBSCAN,  Traditional Density: Center-Based Approach	PPT	2
8	The DBSCAN Algorithm, Strengths and Weaknesses	Board & Chalk	1

Total Number of Hours = 62

Text Books:

1. “Introduction to Data Mining,” Pang-Ning Tan & Michael Steinbach, Vipin Kumar, Pearson, 2^nd edition, 2013.

2. “Data Mining concepts and Techniques,” Jiawei Han, Michel Kamber, Elsevier, 3^rd edition.2011.

Reference Books:

1. “Data Mining Techniques and Applications: An Introduction,”Hongbo Du, Cengage Learning, 2010.
2. “Data Mining: Introductory and Advanced topics,” Dunham, Pearson, 3^rd edition, 2008.
3. “Data Warehousing Data Mining & OLAP,” Alex Berson, Stephen Smith, TMH, 2008.
4. “Data Mining Techniques,”Arun K Pujari, Universities Press, 2005.

Web Resources

1. https://onlinecourses.nptel.ac.in/noc18_cs14/preview (PabitraMitra, IIT, Kharagpur)
2. https://www-users.cs.umn.edu/~kumar001/dmbook/index.php
3. http://hanj.cs.illinois.edu/bk3/bk3_slidesindex.htm

(Signature of the Faculty)                                                                                 (Signature of the HOD)