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Data Mining and Data Warehousing Notes PDF VSSUT – DMDW Notes PDF of Total Complete Notes
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Link: Complete Notes
Module – 1
Data Mining Overview
Data Warehouse and OLAP Technology,
Data Warehouse Architecture, Steps for the Design and Construction of Data Warehouses,
A Three – Tier Data Warehouse Architecture, OLAP, OLAP queries,
metadata repository, Data Preprocessing – Data Integration, and Transformation,
Data Reduction, Data Mining Primitives: What Defines a Data Mining Task?
Task-Relevant Data, The Kind of Knowledge to be Mined, KDD
Link: Module 1 – Data Mining and Data Warehousing Notes
Module – 2
Mining Association Rules in Large Databases
Association Rule Mining,
Market BasketAnalysis: Mining A Road Map, The Apriori Algorithm:
Finding Frequent Itemsets Using Candidate Generation,
Generating Association Rules from Frequent Itemsets,
Improving the Efficiently of Apriori, Mining Frequent Itemsets without Candidate Generation,
Multilevel Association Rules, Approaches toMining Multilevel Association Rules,
Mining Multidimensional Association Rules for Relational Database and Data Warehouses,
Multidimensional Association Rules, Mining Quantitative Association Rules,
MiningDistance-Based Association Rules, From Association Mining to Correlation Analysis
Link: Module 2 – Data Mining and Data Warehousing Notes
Module – 3
What is Classification
What Is Prediction? Issues Regarding
Classification and Prediction, Classification by Decision Tree Induction,
Bayesian Classification, Bayes Theorem, Naïve Bayesian Classification,
Classification by Backpropagation, A Multilayer Feed – Forward Neural Network,
Defining a Network Topology,
Classification Based of Concepts from Association Rule Mining, OtherClassification Methods
Link: Module 3 – Data Mining and Data Warehousing Notes
Module – 4
What Is Cluster Analysis
Types of Data in Cluster Analysis,
A Categorization of Major Clustering Methods, Classical Partitioning Methods:
k-Meansand k – Medoids, Partitioning Methods in Large Databases:
From k-Medoids to CLARANS, Hierarchical Methods,
Agglomerative and Divisive Hierarchical Clustering,
Density – BasedMethods, Wave Cluster: Clustering Using Wavelet Transformation,
CLIQUE:Clustering High – Dimensional Space, Model – Based Clustering Methods,
Statistical Approach, Neural Network Approach.
Link: Module 4 – Data Mining and Data Warehousing Notes
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