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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 Classication and prediction . . . . . . . . . . . . . . . . . . . .
1.4.4 Clustering analysis . . . . . . . . . . . . . . . . . . . . . . . . . .
1.4.5 Evolution and deviation analysis . . . . . . . . . . . . . . . . . .
1.5 Are all of the patterns interesting? . . . . . . . . . . . . . . . . . . . . .
1.6 A classication of data mining systems . . . . . . . . . . . . . . . . . . .
1.7 Major issues in data mining . . . . . . . . . . . . . . . . . . . . . . . . .
1.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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2 Data Warehouse and OLAP Technology for Data Mining
2.1 What is a data warehouse? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 A multidimensional data model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1 From tables to data cubes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.2 Stars, snowakes, and fact constellations: schemas for multidimensional databases
2.2.3 Examples for dening star, snowake, 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 OLAP server architectures: ROLAP vs. MOLAP vs. HOLAP . . . . . . . . . . . .
2.3.4 SQL extensions to support OLAP operations . . . . . . . . . . . . . . . . . . . . .
2.4 Data warehouse implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.1 Ecient computation of data cubes . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.2 Indexing OLAP data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.3 Ecient 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 . . . . . . . . .
2.6 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3 Data Preprocessing
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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4 Primitives for Data Mining
4.1 Data mining primitives: what denes 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 specication . . . . . . . . . . . . . . .
4.2.2 Syntax for specifying the kind of knowledge to be mined . . . . . . .
4.2.3 Syntax for concept hierarchy specication . . . . . . . . . . . . . . .
4.2.4 Syntax for interestingness measure specication . . . . . . . . . . . .
4.2.5 Syntax for pattern presentation and visualization specication . . .
4.2.6 Putting it all together | an example of a DMQL query . . . . . . .
4.3 Designing graphical user interfaces based on a data mining query language .
4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5 Concept Description: Characterization and Comparison
5.1 What is concept description? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Data generalization and summarization-based characterization . . . . . . . . . . .
5.2.1 Data cube approach for data generalization . . . . . . . . . . . . . . . . . .
5.2.2 Attribute-oriented induction . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.3 Presentation of the derived generalization . . . . . . . . . . . . . . . . . . .
5.3 Ecient implementation of attribute-oriented induction . . . . . . . . . . . . . . .
5.3.1 Basic attribute-oriented induction algorithm . . . . . . . . . . . . . . . . . .
5.3.2 Data cube implementation of attribute-oriented induction . . . . . . . . . .
5.4 Analytical characterization: Analysis of attribute relevance . . . . . . . . . . . . .
5.4.1 Why perform attribute relevance analysis? . . . . . . . . . . . . . . . . . . .
5.4.2 Methods of attribute relevance analysis . . . . . . . . . . . . . . . . . . . .
5.4.3 Analytical characterization: An example . . . . . . . . . . . . . . . . . . . .
5.5 Mining class comparisons: Discriminating between dierent classes . . . . . . . . .
5.5.1 Class comparison methods and implementations . . . . . . . . . . . . . . .
5.5.2 Presentation of class comparison descriptions . . . . . . . . . . . . . . . . .
5.5.3 Class description: Presentation of both characterization and comparison . .
5.6 Mining descriptive statistical measures in large databases . . . . . . . . . . . . . .
5.6.1 Measuring the central tendency . . . . . . . . . . . . . . . . . . . . . . . . .
5.6.2 Measuring the dispersion of data . . . . . . . . . . . . . . . . . . . . . . . .
5.6.3 Graph displays of basic statistical class descriptions . . . . . . . . . . . . .
5.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.7.1 Concept description: A comparison with typical machine learning methods
5.7.2 Incremental and parallel mining of concept description . . . . . . . . . . . .
5.7.3 Interestingness measures for concept description . . . . . . . . . . . . . . .
5.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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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 . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2.2 Generating association rules from frequent itemsets . . . . . . . . . . . . . . . . . . . . . . . . .
6.2.3 Variations of the Apriori algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Contents
7 Classication and Prediction
7.1 What is classication? What is prediction? . . . . . . . . . . . . . . . . . .
7.2 Issues regarding classication and prediction . . . . . . . . . . . . . . . . . .
7.3 Classication by decision tree induction . . . . . . . . . . . . . . . . . . . .
7.3.1 Decision tree induction . . . . . . . . . . . . . . . . . . . . . . . . . .
7.3.2 Tree pruning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.3.3 Extracting classication 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 classication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.4.1 Bayes theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.4.2 Naive Bayesian classication . . . . . . . . . . . . . . . . . . . . . .
7.4.3 Bayesian belief networks . . . . . . . . . . . . . . . . . . . . . . . . .
7.4.4 Training Bayesian belief networks . . . . . . . . . . . . . . . . . . . .
7.5 Classication by backpropagation . . . . . . . . . . . . . . . . . . . . . . . .
7.5.1 A multilayer feed-forward neural network . . . . . . . . . . . . . . .
7.5.2 Dening a network topology . . . . . . . . . . . . . . . . . . . . . . .
7.5.3 Backpropagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.5.4 Backpropagation and interpretability . . . . . . . . . . . . . . . . . .
7.6 Association-based classication . . . . . . . . . . . . . . . . . . . . . . . . .
7.7 Other classication methods . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.7.1 k-nearest neighbor classiers . . . . . . . . . . . . . . . . . . . . . .
7.7.2 Case-based reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.7.3 Genetic algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.7.4 Rough set theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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 Classier accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.9.1 Estimating classier accuracy . . . . . . . . . . . . . . . . . . . . . .
7.9.2 Increasing classier accuracy . . . . . . . . . . . . . . . . . . . . . .
7.9.3 Is accuracy enough to judge a classier? . . . . . . . . . . . . . . . .
7.10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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