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III Year II
Semester
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L
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T
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P
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C
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Subject
Code: 16CS6T16
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4
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0
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0
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3
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DATA WAREHOUSING AND MINING
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SYLLABUS
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Learning Objectives:
1.
Students will be enabled to understand and implement classical models and
algorithms in data warehousing and data mining.
2.
They will learn how to analyze the data,
identify the problems, and choose the relevant models and algorithms to apply.
3.
They will further be able to assess the strengths and weaknesses of various
methods and algorithms and to analyze their behavior.
Course Outcomes:
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COURSE
OUTCOME
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COURSE OUTCOMES
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BLOOMS
TAXONOMY LEVEL
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CO-1
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Implement data warehouse for heterogeneous
data.
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Applying
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CO-2
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Analyze real time datasets with basic
summary statistics.
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Analyzing
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CO-3
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Apply different preprocessing methods, Similarity,
Dissimilarity measures for any given raw
data.
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Applying
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CO-4
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Construct a decision tree and resolve the
problem of model
overfitting
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Applying
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CO-5
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Compare Apriori and FP-growth association
rule mining
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Analyzing
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algorithms for frequent itemset generation
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CO-6
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Apply suitable clustering algorithm for the given
data set
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Applying
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The Mapping of CO and PO on 3 point
scale{high-3,Medium-2,Low-1}is:
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PO-
1
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PO-
2
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PO-
3
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PO-
4
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PO-
5
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PO-
6
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PO-
7
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PO-
8
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PO-
9
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PO-
10
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PO-
11
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PO-
12
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PSO-
1
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PSO-
2
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PSO-
3
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CO-1
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3
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2
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2
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1
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1
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-
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-
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-
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-
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-
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-
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-
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2
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1
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-
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CO-2
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3
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3
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3
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1
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1
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-
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-
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-
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-
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-
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-
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-
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2
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1
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-
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CO-3
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3
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3
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3
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1
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1
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-
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-
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-
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-
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-
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-
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-
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2
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1
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-
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CO-4
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3
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3
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3
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1
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1
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-
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-
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-
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-
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-
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-
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-
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2
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1
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-
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CO-5
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3
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3
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3
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1
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1
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-
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-
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-
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-
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-
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-
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-
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2
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1
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-
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CO-6
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3
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3
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3
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1
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1
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-
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-
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-
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-
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-
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-
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-
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2
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1
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-
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UNIT –I
Data Warehouse
and OLAP Technology: An Overview: What Is a Data Warehouse? A Multidimensional
Data Model, Data Warehouse Architecture, Data Warehouse Implementation, From
Data Warehousing to Data Mining. (Han &Kamber)
Data Mining:
Introduction, what is Data Mining?
Motivating challenges, The origins of Data Mining, Data Mining Tasks, Types of Data, Data Quality, Exploring Data, The Iris Dataset, summary statistics
(Tan & Vipin)
UNIT –III
Data
Preprocessing: Aggregation, Sampling, Dimensionality Reduction, Feature Subset
Selection, Feature creation, Discretization and Binarization, Variable
Transformation, Measures of Similarity and Dissimilarity. (Tan & Vipin)
UNIT –IV
Classification:
Basic Concepts, General Approach to solving a classification problem, Decision
Tree Induction: Working of Decision Tree, building a decision tree, methods for
expressing an attribute test conditions, measures for selecting the best split,
Algorithm for decision tree induction.
Model
Overfitting: Due to presence of noise, due to lack of representation samples,
evaluating the performance of classifier: holdout method, random sub sampling,
cross-validation, bootstrap. (Tan & Vipin). Alterative Techniques: Bayes’
Theorem, Naïve Bayesian Classification
UNIT –V
Association
Analysis: Basic Concepts and Algorithms: Problem Definition, Frequent Item Set
Generation using Apriori, Rule Generation, Compact Representation of Frequent
Itemsets, FP- Growth Algorithm. (Tan & Vipin)
UNIT –VI
Cluster
Analysis: Basic Concepts and Algorithms: Overview, What Is Cluster Analysis?
Different Types of Clustering, Different Types of Clusters; K-means: The Basic
K-means Algorithm, K-means Additional Issues, Bisecting K-means, Strengths and
Weaknesses; Agglomerative Hierarchical Clustering: Basic Agglomerative
Hierarchical Clustering Algorithm DBSCAN: Traditional Density Center-Based
Approach, DBSCAN Algorithm, Strengths and Weaknesses. (Tan & Vipin)
TEXT BOOKS:
1. “Introduction
to Data Mining,” Pang-Ning Tan &
Michael Steinbach, Vipin Kumar, Pearson, 2nd edition, 2013.
2. “Data Mining concepts and Techniques,” Jiawei
Han, Michel Kamber, Elsevier, 3rd 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, 3rd edition, 2008.
3.
“Data Warehousing Data Mining
& OLAP,” Alex Berson, Stephen Smith, TMH, 2008.
4.
“Data Mining Techniques,”Arun K Pujari, Universities
Press, 2005.
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