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2.  Data Mining
 Relational Databases
 Data Warehouses
 Transactional Databases
 Data Mining Functionalities
 Mining Frequent Patterns, Associations, and
Correlations
 Cluster Analysis
 Outlier Analysis
 Evolution Analysis
 Classification of Data Mining Systems

3.  Preprocess the Data
 Measuring the Central Tendency
 Measuring the Dispersion of Data
 Data Cleaning
 Missing Values
 Data Integration and Transformation
 Data Integration
 Data Transformation
 Data Reduction
 Data Cube Aggregation
 Dimensionality Reduction
 Numerosity Reduction

4.  Data Warehouse
 A Multidimensional Data Model
 Data Warehouse Architecture
 Data Warehouse Back-End Tools and
Utilities
 Types of OLAP Servers
 Data Warehouse Implementation
 Indexing OLAP Data
 Data Warehouse Usage

5.  Efficient Methods for Data Cube Computation
 Multiway Array Aggregation for Full Cube
Computation
 Precomputing Shell Fragments for Fast High-
Dimensional OLAP
 Computing Cubes with Complex Iceberg
Conditions
 Discovery-Driven Exploration of Data Cubes
 Complex Aggregation at Multiple Granularity
 Constrained Gradient Analysis in Data Cubes
 Attribute-Oriented Induction
 Presentation of the Derived Generalization
 Mining Class Comparisons

6.  Market Basket Analysis
 The Apriori Algorithm
 Mining Frequent Itemsets without Candidate
Generation
 Mining Frequent Itemsets Using Vertical Data
Format
 Mining Closed Frequent Itemsets
 Mining Various Kinds of Association Rules
 Mining Multilevel Association Rules
 Mining Multidimensional Association Rules from
Relational Databases and Data Warehouses
 Constraint-Based Association Mining

7.  Classification
 Prediction

8.  Cluster Analysis
 Types of Data in Cluster Analysis
 A Categorization of Major 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
 Density-Based Local Outlier Detection
 Deviation-Based Outlier Detection

9.  Mining Data Streams
 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
 Mining Time-Series Data
 Sequential Pattern Mining
 Scalable Methods for Mining Sequential Patterns
 Constraint-Based Mining of Sequential Patterns
 Periodicity Analysis for Time-Related Sequence
Data

10.  Graph Mining
 Methods for Mining Frequent Subgraphs
 Social Network Analysis
 Social Network
 Characteristics of Social Networks
 Link Mining: Tasks and Challenges
 ILP Approach to Multirelational Classification
 Tuple ID Propagation
 Multirelational Classification
 Multirelational Clustering

11.  Multidimensional Analysis and Descriptive
Mining of Complex Data Objects
 Generalization of Structured Data
 Aggregation and Approximation in Spatial and
Multimedia Data Generalization
 Construction and Mining of Object Cubes
 Generalization-Based Mining of Plan Databases
by Divide-and-Conquer
 Spatial Data Mining atial Data Cube Construction
and Spatial OLAP
 Mining Spatial Association and Co-location
Patterns

12.  Spatial Classification and Spatial Trend Analysis
 Multimedia Data Mining
 Classification and Prediction Analysis of
Multimedia Data
 Audio and Video Data Mining
 Text Mining
 Mining the World Wide Web
 Mining the Web Page Layout Structure
 Mining Multimedia Data on the Web
 Web Usage Mining

13.  Data Mining for Financial Data Analysis Data
Mining for the Retail Industry
 Data Mining for the Telecommunication Industry
 Data Mining for Biological Data Analysis
 Data Mining in Other Scientific Applications
 Data Mining for Intrusion Detection
 Theoretical Foundations of Data Mining
 Statistical Data Mining
 Visual and Audio Data Mining
 Trends in Data Mining

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