[分享]经典的数据挖掘课件

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收藏 2006-04-26

J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000
Contents:
1 Introduction
1.1 What motivated data mining? Why is it important?
1.2 So, what is data mining?
1.3 Data mining-on what kind of data?
1.3.1 Relational databases
1.3.2 Data warehouses
1.3.3 Transactional databases
1.3.4 Advanced database systems and advanced database applications
1.4 Data mining functionalities-what kinds of patterns can be mined?
1.4.1 Concept/class description: characterization and discrimination
1.4.2 Association analysis
1.4.3 Classification and prediction
1.4.4 Cluster analysis
1.4.5 Outlier analysis
1.4.6 Evolution analysis
1.5 Are all of the patterns interesting?
1.6 Classification of data mining systems
1.7 Major issues in data mining
1.8 Summary

2 Data Warehouse and OLAP Technology for Data Mining
2.1 What is a data warehouse?
2.1.1 Differences between operational database systems and data warehouses
2.1.2 But, why have a separate data warehouse?
2.2 A multidimensional data model
2.2.1 From tables and spreadsheets to data cubes
2.2.2 Stars, snowflakes, and fact constellations: schemas for multidimensional databases
2.2.3 Examples for defining star, snowflake and fact constellation schemas
2.2.4 Measures: their categorization and computation
2.2.5 Introducing concept hierarchies
2.2.6 OLAP operations in the multidimensional data model
2.2.7 A starnet query model for querying multidimensional databases
2.3 Data warehouse architecture
2.3.1 Steps for the design and construction of data warehouses
2.3.2 A three-tier data warehouse architecture
2.3.3 Types of OLAP servers: ROLAP versus MOLAP versus HOLAP
2.4 Data warehouse implementation
2.4.1 Efficient computation of data cubes
2.4.2 Indexing OLAP data
2.4.3 Efficient processing of OLAP queries
2.4.4 Metadata repository
2.4.5 Data warehouse back-end tools and utilities
2.5 Further development of data cube technology
2.5.1 Discovery-driven exploration of data cubes
2.5.2 Complex aggregation at multiple granularities: multifeature cubes
2.5.3 Other developments
2.6 From data warehousing to data mining
2.6.1 Data warehouse usage
2.6.2 From on-line analytical processing to on-line analytical mining
2.7 Summary

3 Data Preparation
3.1 Why preprocess the data?
3.2 Data cleaning
3.2.1 Missing values
3.2.2 Noisy data
3.2.3 Inconsistent data
3.3 Data integration and transformation
3.3.1 Data integration
3.3.2 Data transformation
3.4 Data reduction
3.4.1 Data cube aggregation
3.4.2 Dimensionality reduction
3.4.3 Data compression
3.4.4 Numerosity reduction
3.5 Discretization and concept hierarchy generation
3.5.1 Discretization and concept hierarchy generation for numeric data
3.5.2 Concept hierarchy generation for categorical data
3.6 Summary

4 Data Mining Primitives, Languages, and System Architectures
4.1 Data mining primitives: what defines a data mining task?
4.1.1 Task-relevant data
4.1.2 The kind of knowledge to be mined
4.1.3 Background knowledge: concept hierarchies
4.1.4 Interestingness measures
4.1.5 Presentation and visualization of discovered patterns
4.2 A data mining query language
4.2.1 Syntax for task-relevant data specification
4.2.2 Syntax for specifying the kind of knowledge to be mined
4.2.3 Syntax for concept hierarchy specification
4.2.4 Syntax for interestingness measure specification
4.2.5 Syntax for pattern presentation and visualization specification
4.2.6 Putting it all together-an example of a DMQL query
4.2.7 Other data mining languages and the standardization of data mining primitives
4.3 Designing graphical user interfaces based on a data mining query language
4.4 Architecture of data mining systems
4.5 Summary

5 Concept Description: Characterization and Comparison
5.1 What is concept description?
5.2 Data generalization and summarization-based characterization
5.2.1 Attribute-oriented induction
5.2.2 Efficient implementation of attribute-oriented induction
5.2.3 Presentation of the derived generalization
5.3 Analytical characterization: analysis of attribute relevance
5.3.1 Why perform attribute relevance analysis?
5.3.2 Methods of attribute relevance analysis
5.3.3 Analytical characterization: an example
5.4 Mining class comparisons: discriminating between different classes
5.4.1 Class comparison methods and implementations
5.4.2 Presentation of class comparison descriptions
5.4.3 Class description: presentation of both characterization and comparison
5.5 Mining descriptive statistical measures in large databases
5.5.1 Measuring the central tendency
5.5.2 Measuring the dispersion of data
5.5.3 Graph displays of basic statistical class descriptions
5.6 Discussion
5.6.1 Concept description: a comparison with typical machine learning methods
5.6.2 Incremental and parallel mining of concept description
5.7 Summary

6 Mining Association Rules in Large Databases
6.1 Association rule mining
6.1.1 Market basket analysis: a motivating example for association rule mining
6.1.2 Basic concepts
6.1.3 Association rule mining: a road map
6.2 Mining single-dimensional Boolean association rules from transactional databases
6.2.1 The Apriori algorithm: finding frequent itemsets using candidate generation
6.2.2 Generating association rules from frequent itemsets
6.2.3 Improving the efficiency of Apriori
6.2.4 Mining frequent itemsets without candidate generation
6.2.5 Iceberg queries
6.3 Mining multilevel association rules from transaction databases
6.3.1 Multilevel association rules
6.3.2 Approaches to mining multilevel association rules
6.3.3 Checking for redundant multilevel association rules
6.4 Mining multidimensional association rules from relational databases and data warehouses
6.4.1 Multidimensional association rules
6.4.2 Mining multidimensional association rules using static discretization of quantitative attributes
6.4.3 Mining quantitative association rules
6.4.4 Mining distance-based association rules
6.5 From association mining to correlation analysis
6.5.1 Strong rules are not necessarily interesting: an example
6.5.2 From association analysis to correlation analysis
6.6 Constraint-based association mining
6.6.1 Metarule-guided mining of association rules
6.6.2 Mining guided by additional rule constraints
6.7 Summary

7 Classification and Prediction
7.1 What is classification? What is prediction?
7.2 Issues regarding classification and prediction
7.2.1 Preparing data for classification and prediction
7.2.2 Comparing classification methods
7.3 Classification by decision tree induction
7.3.1 Decision tree induction
7.3.2 Tree pruning
7.3.3 Extracting classification rules from decision trees
7.3.4 Enhancements to basic decision tree induction
7.3.5 Scalability and decision tree induction
7.3.6 Integrating data warehousing techniques and decision tree induction
7.4 Bayesian classification
7.4.1 Bayes theorem
7.4.2 Na?ve Bayesian classification
7.4.3 Bayesian belief networks
7.4.4 Traning Bayesian belief networks
7.5 Classification by backpropagation
7.5.1 A multiplayer feed-forward neural network
7.5.2 Defining a network topology
7.5.3 Backpropagation
7.5.4 Backpropagation and interpretability
7.6 Classification based on concepts from association rule mining
7.7 Other classification methods
7.7.1 k-nearest neighbor classifiers
7.7.2 Case-based reasoning
7.7.3 Genetic algorithms
7.7.4 Rough set approach
7.7.5 Fuzzy set approaches
7.8 Prediction
7.8.1 Linear and multiple regression
7.8.2 Nonlinear regression
7.8.3 Other regression models
7.9 Classifier accuracy
7.9.1 Estimating classifier accuracy
7.9.2 Increasing classifier accuracy
7.9.3 Is accuracy enough to judge a classifier
7.10 Summary

8 Cluster Analysis
8.1 What is cluster analysis?
8.2 Types of data in clustering analysis
8.2.1 Interval-scaled variables
8.2.2 Binary variables
8.2.3 Nominal, ordinal, and ratio-scaled variables
8.2.4 Variables of mixed types
8.3 A categorization of major clustering methods
8.4 Partitioning methods
8.4.1 Classical partitioning methods: k-means and k-medoids
8.4.2 Partitioning methods in large databases: from k-medoids to CLARANS
8.5 Hierarchical methods
8.5.1 Agglomerative and divisive hierarchical clustering
8.5.2 BIRCH: Balanced Iterative Reducing and Clustering using Hierarchies
8.5.3 CURE: Clustering Using REpresentatives
8.5.4 CHAMELEON: A hierarchical clustering algorithm using dynamic modeling
8.6 Density-based methods
8.6.1 DBSCAN: A density-based clustering method based on connected regions with sufficiently high density
8.6.2 OTPICS: Ordering Points to Identify the Clustering Structure
8.6.3 DENCLUE: Clustering based on density distribution functions
8.7 Grid-based methods
8.7.1 STING: A STatistcal INformation Grid approach
8.7.2 WaveCluster: Clustering using wavelet transformations
8.7.3 CLIQUE: Clustering high-dimensional space
8.8 Model-based clustering methods
8.8.1 Statistical approach
8.8.2 Neural network approach
8.9 Outlier analysis
8.9.1 Statistical-based outlier detection
8.9.2 Distance-based outlier detection
8.9.3 Deviation-based outlier detection
8.10 Summary

9 Mining Complex Types of Data
9.1 Multidimensional analysis and descriptive mining of complex data objects
9.1.1 Generalization of structured data
9.1.2 Aggregation and approximation in spatial and multimedia data generalization
9.1.3 Generalization of object identifiers and class/subclass hierarchies
9.1.4 Generalization of class composition hierarchies
9.1.5 Construction and mining of object cubes
9.1.6 Generalization-based mining of plan databases by divide-and-conquer
9.2 Mining spatial databases
9.2.1 Spatial data cube construction and spatial OLAP
9.2.2 Spatial association analysis
9.2.3 Spatial clustering methods
9.2.4 Spatial classification and spatial trend analysis
9.2.5 Mining raster databases
9.3 Mining multimedia databases
9.3.1 Similarity search in multimedia data
9.3.2 Multidimensional analysis of multimedia data
9.3.3 Classification and prediction analysis of multimedia data
9.3.4 Mining associations in multimedia data
9.4 Mining time-series and sequence data
9.4.1 Trend analysis
9.4.2 Similarity search in time-series analysis
9.4.3 Sequential pattern mining
9.4.4 Periodicity analysis
9.5 Mining text databases
9.5.1 Text data analysis and information retrieval
9.5.2 Text mining: keyword-based association and document classification
9.6 Mining the World-Wide Web
9.6.1 Mining the Web's link structures to identify authoritative Web pages
9.6.2 Automatic classification of Web documents
9.6.3 Construction of a multilayered Web information base
9.6.4 Web usage mining
9.7 Summary

10 Data Mining Applications and Trends in Data Mining
10.l Data mining applications
10.1.1 Data mining for biomedical and DNA data analysis
10.1.2 Data mining for financial data analysis
10.1.3 Data mining for the retail industry
10.1.4 Data mining for the telecommunication industry
10.2 Data mining system products and research prototypes
10.2.1 How to choose a data mining system
10.2.2 Examples of commercial data mining systems
10.3 Additional themes on data mining
10.3.1 Visual and audio data mining
10.3.2 Scientific and statistical data mining
10.3.3 Theoretical foundations of data mining
10.3.4 Data mining and intelligent query answering
10.4 Social impacts of data mining
10.4.1 Is data mining a hype or a persistent, steadily growing business?
10.4.2 Is data mining merely managers' business or everyone's business?
10.4.3 Is data mining a threat to privacy and data security?
10.5 Trends in data mining
10.6 Summary
Appendix A An Introduction to Microsoft's OLE DB for Data Mining
Appendix B An Introduction to DBMiner