JNTUH
Ref: Introduction To Data Mining P N Tan M Steinbach V Kumar
1.0 what is data mining
1.1 KDD
1.2 Challenges
1.3 Data mining Tasks
1.4 Preprocessing
1.7 Dimensionality reduction
1.8 Feature subset selection
1.9 Discretization and Binaryzation
1.10 Variable Transformation
1.11 Measures of Similarity and Dissimilarity
2.1 Association Analysis Problem Definition
2.2 Frequent Itemset Generation
2.3 Apriori Principle
2.5 Rule generation
2.6 Apriori Algorithm
2.7 FP Growth algorithm
2.8 Compact Representation of Frequent Itemset
2.8a Compact Representation of Frequent Itemset
3.1 Classification - Problem definition
3.2 General Approaches to solving a classification problem & 3.3 Evaluation of Classifiers
3.4 Classification technique
3.5 Decision tree construction
3.6 Methods for Expressing Attribute Test Conditions
3.7 Measures for Selecting the Best Split,
3.8 Algorithm for Decision tree Induction
3.9 Naive bayes
3.10 Bayesian Belief Networks
3.11 Nearest neighbor
4.1 Clustering Overview
4.2 Partitioning Clustering
4.3 K-means algorithm
4.4 K-Means Additional issues
4.4a PAM Algorithm
4.5 Basic Agglomerative Hierarchical Clustering Algorithm
4.6 specific techniques (short notes without examples)
4.6 specific techniques (with examples)
4.7 Key Issues in Hierarchical Clustering
4.8 Strengths and Weakness
4.9 Outlier Analysis
5.0 Web usage Mining
5.2 Mining the World Wide Web
UNIT-1 PDF
UNIT-2 PDF
UNIT-3 PDF
UNIT-4 PDF
UNIT-5 PDF
Anna University
Ref: Data Mining: Concepts and Techniques - Jiawei Han, Micheline Kamber
1.1 Data Warehouses Introduction
1.2 Multidimensional model
1.3 Data Warehouse Architecture
1.4 Data Warehouse Implementation
1.6 From Data Warehousing to Data Mining
2.1 Needfor Data Cleaning
2.2 Data Cleaning
2.3 Data Integration
2.4 Data Transformation
2.5 Data Reduction
2.6 Data Discretizationand Concept Hierarchy Generation for Numerical Data
Ref: Introduction To Data Mining P N Tan M Steinbach V Kumar
1.0 what is data mining
1.1 KDD
1.2 Challenges
1.3 Data mining Tasks
1.4 Preprocessing
1.7 Dimensionality reduction
1.8 Feature subset selection
1.9 Discretization and Binaryzation
1.10 Variable Transformation
1.11 Measures of Similarity and Dissimilarity
2.1 Association Analysis Problem Definition
2.2 Frequent Itemset Generation
2.3 Apriori Principle
2.5 Rule generation
2.6 Apriori Algorithm
2.7 FP Growth algorithm
2.8 Compact Representation of Frequent Itemset
2.8a Compact Representation of Frequent Itemset
3.1 Classification - Problem definition
3.2 General Approaches to solving a classification problem & 3.3 Evaluation of Classifiers
3.4 Classification technique
3.5 Decision tree construction
3.6 Methods for Expressing Attribute Test Conditions
3.7 Measures for Selecting the Best Split,
3.8 Algorithm for Decision tree Induction
3.9 Naive bayes
3.10 Bayesian Belief Networks
3.11 Nearest neighbor
4.1 Clustering Overview
4.2 Partitioning Clustering
4.3 K-means algorithm
4.4 K-Means Additional issues
4.4a PAM Algorithm
4.5 Basic Agglomerative Hierarchical Clustering Algorithm
4.6 specific techniques (short notes without examples)
4.6 specific techniques (with examples)
4.7 Key Issues in Hierarchical Clustering
4.8 Strengths and Weakness
4.9 Outlier Analysis
5.0 Web usage Mining
5.2 Mining the World Wide Web
UNIT-1 PDF
UNIT-2 PDF
UNIT-3 PDF
UNIT-4 PDF
UNIT-5 PDF
Anna University
Ref: Data Mining: Concepts and Techniques - Jiawei Han, Micheline Kamber
1.1 Data Warehouses Introduction
1.2 Multidimensional model
1.3 Data Warehouse Architecture
1.4 Data Warehouse Implementation
1.6 From Data Warehousing to Data Mining
2.1 Needfor Data Cleaning
2.2 Data Cleaning
2.3 Data Integration
2.4 Data Transformation
2.5 Data Reduction
2.6 Data Discretizationand Concept Hierarchy Generation for Numerical Data
2.8 Data Mining Query Language
2.9 Designing Graphical User Interfaces Based On a Data MiningQuery Language
2.11 Concept Description
2.14 Class Comparison
3.1 Mining Frequent Patterns, Associations, and Correlations
3.2 Single-Dimensional Boolean Association Rules from Transactional Databases-The Apriori Algorithm
3.3 Mining Multilevel Association Rules
3.4 Mining Multidimensional Association Rules
4.1 Classification and Prediction
4.2 Issues regarding classification and prediction
4.3 Decision tree
4.4 Bayesian classification
4.5 Association Rule based classification
4.6 other classification methods
4.7 Prediction
4.9 Cluster Analysis
4.11 Categorization of Clustering Methods
4.13 Outlier Analysis
2.11 Concept Description
2.14 Class Comparison
3.1 Mining Frequent Patterns, Associations, and Correlations
3.2 Single-Dimensional Boolean Association Rules from Transactional Databases-The Apriori Algorithm
3.3 Mining Multilevel Association Rules
3.4 Mining Multidimensional Association Rules
4.1 Classification and Prediction
4.2 Issues regarding classification and prediction
4.3 Decision tree
4.4 Bayesian classification
4.5 Association Rule based classification
4.6 other classification methods
4.7 Prediction
4.9 Cluster Analysis
4.11 Categorization of Clustering Methods
4.13 Outlier Analysis
No comments:
Post a Comment