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Unit: 1
Introduction to Data mining
Data Architecture
Data-Warehouses
Relational Databases
Transactional Databases
Advanced Data and Information Systems and Advanced Applications
Data Mining Functionalities
Classification of Data Mining Systems
Data Mining Task Primitives
Integration of a Data Mining System with a DataWarehouse System
Major Issues in Data Mining
Performance issues in Data Mining
Introduction to Data Preprocess
Descriptive Data Summarization
Measuring the Dispersion of Data
Graphic Displays of Basic Descriptive Data Summaries
Data Cleaning
Noisy Data
Data Cleaning Process
Data Integration and Transformation
Data Transformation
Data Reduction
Dimensionality Reduction
Numerosity Reduction
Clustering and Sampling
Data Discretization and Concept Hierarchy Generation
Concept Hierarchy Generation for Categorical Data
Unit: 2
Introduction to Data warehouses
Differences between Operational Database Systems and Data Warehouses
A Multidimensional Data Model
A Multidimensional Data Model
Data Warehouse Architecture
The Process of Data Warehouse Design
A Three-Tier Data Warehouse Architecture
Data Warehouse Back-End Tools and Utilities
Types of OLAP Servers: ROLAP versus MOLAP versus HOLAP
Data Warehouse Implementation
Data Warehousing to Data Mining
On-Line Analytical Processing to On-Line Analytical Mining
Methods for Data Cube Computation
Multiway Array Aggregation for Full Cube Computation
Star-Cubing: Computing Iceberg Cubes Using a Dynamic Star-tree Structure
Pre-computing Shell Fragments for Fast High-Dimensional OLAP
Driven Exploration of Data Cubes
Complex Aggregation at Multiple Granularity: Multi feature Cubes
Attribute-Oriented Induction
Attribute-Oriented Induction for Data Characterization
Efficient Implementation of Attribute-Oriented Induction
Mining Class Comparisons: Discriminating between Different Classes
Unit: 3
Frequent patterns
The Apriori Algorithm
Efficient and scalable frequently itemset mining methods
Mining Frequent Itemsets Using Vertical Data Format
Mining Multilevel Association Rules
Mining Multidimensional Association Rules
Mining Quantitative Association Rules
Association Mining to Correlation Analysis
Constraint-Based Association Mining
Introduction to classification and prediction
Preparing the Data for Classification and Prediction
Comparing Classification and Prediction Methods
Classification by Decision Tree Induction
Decision Tree Induction
Tree Pruning
Scalability and Decision Tree Induction
Bayesian Classification
Naive Bayesian Classification
Bayesian Belief Networks
Training Bayesian Belief Networks
Using IF-THEN Rules for Classification
Rule Extraction from a Decision Tree
Rule Induction Using a Sequential Covering Algorithm
Rule Pruning
Introduction to Back propagation
A Multilayer Feed-Forward Neural Network
Defining a Network Topology
Support Vector Machines
Associative Classification: Classification by Association Rule Analysis
Evaluating the Accuracy of a Classifier or Predictor
Genetic Algorithms
Bagging
Adaboost, a boosting algorithm
Estimating Confidence Intervals
ROC Curves
Challenges in Clustering
Unit: 4
Introduction to Cluster
Types of Data in Cluster Analysis
Interval-Scaled Variables
Binary Variables
Categorical Variables
Ordinal Variables
Ratio-Scaled Variables
Vector Objects
A Categorization of Major Clustering Methods
Classical Partitioning Methods: k-Means and k-Medoids
Representative Object-Based Technique
Hierarchical Methods
ROCK: A Hierarchical Clustering Algorithm for Categorical Attributes
Chameleon: A Hierarchical Clustering Algorithm Using Dynamic Modeling
Density-Based Methods
DENCLUE: Clustering Based on Density Distribution Functions
Grid-Based Methods
STING: Statistical Information Grid
Wave Cluster: Clustering Using Wavelet Transformation
Model-Based Clustering Methods
Conceptual Clustering
Neural Network Approach
Clustering High-Dimensional Data
CLIQUE: A Dimension-Growth Subspace Clustering Method
PROCLUS: A Dimension-Reduction Subspace Clustering Method
Frequent Pattern–Based Clustering Methods
Constraint-Based Cluster Analysis
Clustering with Obstacle Objects
User-Constrained Cluster Analysis
Semi-supervised Cluster Analysis
Outlier Analysis
Statistical Distribution-Based Outlier Detection
Distance-Based Outlier Detection
Deviation-Based Outlier Detection
Mining Data stream
Methodologies for Stream Data Processing and Stream Data Systems
Stream OLAP and Stream Data Cubes
Frequent-Pattern Mining in Data Streams
Classification of Dynamic Data Streams
Very Fast Decision Tree (VFDT) and Concept-adapting Very Fast Decision Tree (CVFDT)
Clustering Evolving Data Streams
STREAM: A k-Medians-based Stream Clustering Algorithm
Mining Time-Series Data
Similarity Search in Time-Series Analysis
similarity-search-methods
Mining Sequence Patterns in Transactional Databases
Scalable Methods for Mining Sequential Patterns
SPADE: An A priori-Based Vertical Data Format Sequential Pattern Mining Algorithm
Prefix Span: Prefix-Projected Sequential Pattern Growth
Mining Closed Sequential Patterns
Constraint-Based Mining of Sequential Patterns
Periodicity Analysis for Time-Related Sequence Data
Mining Sequence Patterns in Biological Data
Hidden Markov Model for Biological Sequence Analysis
Forward Algorithm
Viterbi Algorithm
Baum-Welch Algorithm
Unit: 5
Introduction to Graph Mining
Methods for Mining Frequent Sub graphs
Pattern-Growth Approach
Mining Variant and Constrained Substructure Patterns
Graph Indexing with Discriminative Frequent Substructures
Substructure Similarity Search in Graph Databases
Classification and Cluster Analysis Using Graph Patterns
Social Network Analysis
Characteristics of Social Networks
Link Mining: Tasks and Challenges
Link Prediction: What Edges Will Be Added to the Network
Mining Customer Networks for Viral Marketing
Mining Newsgroups Using Networks
Community Mining from Multi relational Networks
Multi-relational Data Mining
ILP Approach to Multi-relational Classification
Tuple ID Propagation
Multi-relational Classification Using Tuple ID Propagation
Multi-relational Clustering with User Guidance
Multidimensional Analysis and Descriptive Mining of Complex Data Objects
Generalization of Structured Data
Aggregation and Approximation in Spatial and Multimedia Data Generalization
Generalization of Object Identifiers and Class/Subclass Hierarchies
Spatial Data Mining
Spatial Data Cube Construction and Spatial OLAP
Mining Spatial Association and Co-location Patterns
Spatial Clustering Methods
Multimedia Data Mining
Classification and Prediction Analysis of Multimedia Data
Mining Associations in Multimedia Data
Audio and Video Data Mining
Text Mining
Text Data Analysis and Information Retrieval
Text Retrieval Methods
Text Indexing and Query Processing Techniques
Dimensionality Reduction for Text
Text Mining Approaches
Document Clustering Analysis
Mining the World Wide Web
Mining the Web Page Layout Structure
Mining the Web’s Link Structures to Identify Authoritative Web Pages
Mining Multimedia Data on the Web
Web Usage Mining
Introduction to Data Mining Applications
Data Mining for the Telecommunication Industry
Data Mining for Biological Data Analysis
Data Mining in Other Scientific Applications
Data Mining for Intrusion Detection
Data Mining System Products and Research Prototypes
Examples of Commercial Data Mining Systems
Theoretical Foundations of Data Mining
Statistical Data Mining
Visual and Audio Data Mining
Data Mining and Collaborative Filtering
Ubiquitous and Invisible Data Mining
Data Mining, Privacy, and Data Security
Trends in Data Mining
General Theory of Pipeline
Branch :
Computer Science and Engineering
Subject :
Datamining and data ware housing
Unit :
Unit: 2
A Multidimensional Data Model
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