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9.4 Classification Using Frequent Patterns 415
have been studied. Three admissible kernel functions are
Polynomial kernel of degree h: K(Xi , Xj)= (Xi Xj + 1)h
Gaussian radial basis function kernel: K(Xi , Xj)= eXiXj2/2
2
Sigmoid kernel: K(Xi , Xj)= tanh(Xi Xj )
Each of these results in a different nonlinear classifier in
(the original) input space.Neural network aficionados will be
interested to note that the resulting decision hyper-planes found
for nonlinear SVMs are the same type as those found by other
well-knownneural network classifiers. For instance, an SVM with a
Gaussian radial basis func-tion (RBF) gives the same decision
hyperplane as a type of neural network known asa radial basis
function network. An SVM with a sigmoid kernel is equivalent to a
simpletwo-layer neural network known as a multilayer perceptron
(with no hidden layers).
There are no golden rules for determining which admissible
kernel will result in themost accurate SVM. In practice, the kernel
chosen does not generally make a largedifference in resulting
accuracy. SVM training always finds a global solution, unlikeneural
networks, such as backpropagation, where many local minima usually
exist(Section 9.2.3).
So far, we have described linear and nonlinear SVMs for binary
(i.e., two-class) clas-sification. SVM classifiers can be combined
for the multiclass case. See Section 9.7.1 forsome strategies, such
as training one classifier per class and the use of
error-correctingcodes.
A major research goal regarding SVMs is to improve the speed in
training and testingso that SVMs may become a more feasible option
for very large data sets (e.g., millionsof support vectors). Other
issues include determining the best kernel for a given data setand
finding more efficient methods for the multiclass case.
9.4 Classification Using Frequent PatternsFrequent patterns show
interesting relationships between attributevalue pairs thatoccur
frequently in a given data set. For example, we may find that the
attributevaluepairs age= youth and credit = OK occur in 20% of data
tuples describing AllElectronicscustomers who buy a computer. We
can think of each attributevalue pair as an item,so the search for
these frequent patterns is known as frequent pattern mining or
frequentitemset mining. In Chapters 6 and 7, we saw how association
rules are derived fromfrequent patterns, where the associations are
commonly used to analyze the purchas-ing patterns of customers in a
store. Such analysis is useful in many decision-makingprocesses
such as product placement, catalog design, and cross-marketing.
In this section, we examine how frequent patterns can be used
for classification.Section 9.4.1 explores associative
classification, where association rules are generatedfrom frequent
patterns and used for classification. The general idea is that we
can searchfor strong associations between frequent patterns
(conjunctions of attributevalue
Front Cover Data Mining: Concepts and
TechniquesCopyrightDedicationTable of ContentsForewordForeword to
Second EditionPrefaceAcknowledgmentsAbout the AuthorsChapter 1.
Introduction1.1 Why Data Mining?1.2 What Is Data Mining?1.3 What
Kinds of Data Can Be Mined?1.4 What Kinds of Patterns Can Be
Mined?1.5 Which Technologies Are Used?1.6 Which Kinds of
Applications Are Targeted?1.7 Major Issues in Data Mining1.8
Summary1.9 Exercises1.10 Bibliographic Notes
Chapter 2. Getting to Know Your Data2.1 Data Objects and
Attribute Types2.2 Basic Statistical Descriptions of Data2.3 Data
Visualization2.4 Measuring Data Similarity and Dissimilarity2.5
Summary2.6 Exercises2.7 Bibliographic Notes
Chapter 3. Data Preprocessing3.1 Data Preprocessing: An
Overview3.2 Data Cleaning3.3 Data Integration3.4 Data Reduction3.5
Data Transformation and Data Discretization3.6 Summary3.7
Exercises3.8 Bibliographic Notes
Chapter 4. Data Warehousing and Online Analytical Processing4.1
Data Warehouse: Basic Concepts4.2 Data Warehouse Modeling: Data
Cube and OLAP4.3 Data Warehouse Design and Usage4.4 Data Warehouse
Implementation4.5 Data Generalization by Attribute-Oriented
Induction4.6 Summary4.7 Exercises4.8 Bibliographic Notes
Chapter 5. Data Cube Technology5.1 Data Cube Computation:
Preliminary Concepts5.2 Data Cube Computation Methods5.3 Processing
Advanced Kinds of Queries by Exploring Cube Technology5.4
Multidimensional Data Analysis in Cube Space5.5 Summary5.6
Exercises5.7 Bibliographic Notes
Chapter 6. Mining Frequent Patterns, Associations, and
Correlations: Basic Concepts and Methods6.1 Basic Concepts6.2
Frequent Itemset Mining Methods6.3 Which Patterns Are
Interesting?Pattern Evaluation Methods6.4 Summary6.5 Exercises6.6
Bibliographic Notes
Chapter 7. Advanced Pattern Mining7.1 Pattern Mining: A Road
Map7.2 Pattern Mining in Multilevel, Multidimensional Space7.3
Constraint-Based Frequent Pattern Mining7.4 Mining High-Dimensional
Data and Colossal Patterns7.5 Mining Compressed or Approximate
Patterns7.6 Pattern Exploration and Application7.7 Summary7.8
Exercises7.9 Bibliographic Notes
Chapter 8. Classification: Basic Concepts8.1 Basic Concepts8.2
Decision Tree Induction8.3 Bayes Classification Methods8.4
Rule-Based Classification8.5 Model Evaluation and Selection8.6
Techniques to Improve Classification Accuracy8.7 Summary8.8
Exercises8.9 Bibliographic Notes
Chapter 9. Classification: Advanced Methods9.1 Bayesian Belief
Networks9.2 Classification by Backpropagation9.3 Support Vector
Machines9.4 Classification Using Frequent Patterns9.5 Lazy Learners
(or Learning from Your Neighbors)9.6 Other Classification
Methods9.7 Additional Topics Regarding Classification9.8 Summary9.9
Exercises9.10 Bibliographic Notes
Chapter 10. Cluster Analysis: Basic Concepts and Methods10.1
Cluster Analysis10.2 Partitioning Methods10.3 Hierarchical
Methods10.4 Density-Based Methods10.5 Grid-Based Methods10.6
Evaluation of Clustering10.7 Summary10.8 Exercises10.9
Bibliographic Notes
Chapter 11. Advanced Cluster Analysis11.1 Probabilistic
Model-Based Clustering11.2 Clustering High-Dimensional Data11.3
Clustering Graph and Network Data11.4 Clustering with
Constraints11.5 Summary11.6 Exercises11.7 Bibliographic Notes
Chapter 12. Outlier Detection12.1 Outliers and Outlier
Analysis12.2 Outlier Detection Methods12.3 Statistical
Approaches12.4 Proximity-Based Approaches12.5 Clustering-Based
Approaches12.6 Classification-Based Approaches12.7 Mining
Contextual and Collective Outliers12.8 Outlier Detection in
High-Dimensional Data12.9 Summary12.10 Exercises12.11 Bibliographic
Notes
Chapter 13. Data Mining Trends and Research Frontiers13.1 Mining
Complex Data Types13.2 Other Methodologies of Data Mining13.3 Data
Mining Applications13.4 Data Mining and Society13.5 Data Mining
Trends13.6 Summary13.7 Exercises13.8 Bibliographic Notes
BibliographyIndexFront Cover Data Mining: Concepts and
TechniquesCopyrightDedicationTable of ContentsForewordForeword to
Second EditionPrefaceAcknowledgmentsAbout the AuthorsChapter 1.
Introduction1.1 Why Data Mining?1.2 What Is Data Mining?1.3 What
Kinds of Data Can Be Mined?1.4 What Kinds of Patterns Can Be
Mined?1.5 Which Technologies Are Used?1.6 Which Kinds of
Applications Are Targeted?1.7 Major Issues in Data Mining1.8
Summary1.9 Exercises1.10 Bibliographic Notes
Chapter 2. Getting to Know Your Data2.1 Data Objects and
Attribute Types2.2 Basic Statistical Descriptions of Data2.3 Data
Visualization2.4 Measuring Data Similarity and Dissimilarity2.5
Summary2.6 Exercises2.7 Bibliographic Notes
Chapter 3. Data Preprocessing3.1 Data Preprocessing: An
Overview3.2 Data Cleaning3.3 Data Integration3.4 Data Reduction3.5
Data Transformation and Data Discretization3.6 Summary3.7
Exercises3.8 Bibliographic Notes
Chapter 4. Data Warehousing and Online Analytical Processing4.1
Data Warehouse: Basic Concepts4.2 Data Warehouse Modeling: Data
Cube and OLAP4.3 Data Warehouse Design and Usage4.4 Data Warehouse
Implementation4.5 Data Generalization by Attribute-Oriented
Induction4.6 Summary4.7 Exercises4.8 Bibliographic Notes
Chapter 5. Data Cube Technology5.1 Data Cube Computation:
Preliminary Concepts5.2 Data Cube Computation Methods5.3 Processing
Advanced Kinds of Queries by Exploring Cube Technology5.4
Multidimensional Data Analysis in Cube Space5.5 Summary5.6
Exercises5.7 Bibliographic Notes
Chapter 6. Mining Frequent Patterns, Associations, and
Correlations: Basic Concepts and Methods6.1 Basic Concepts6.2
Frequent Itemset Mining Methods6.3 Which Patterns Are
Interesting?Pattern Evaluation Methods6.4 Summary6.5 Exercises6.6
Bibliographic Notes
Chapter 7. Advanced Pattern Mining7.1 Pattern Mining: A Road
Map7.2 Pattern Mining in Multilevel, Multidimensional Space7.3
Constraint-Based Frequent Pattern Mining7.4 Mining High-Dimensional
Data and Colossal Patterns7.5 Mining Compressed or Approximate
Patterns7.6 Pattern Exploration and Application7.7 Summary7.8
Exercises7.9 Bibliographic Notes
Chapter 8. Classification: Basic Concepts8.1 Basic Concepts8.2
Decision Tree Induction8.3 Bayes Classification Methods8.4
Rule-Based Classification8.5 Model Evaluation and Selection8.6
Techniques to Improve Classification Accuracy8.7 Summary8.8
Exercises8.9 Bibliographic Notes
Chapter 9. Classification: Advanced Methods9.1 Bayesian Belief
Networks9.2 Classification by Backpropagation9.3 Support Vector
Machines9.4 Classification Using Frequent Patterns9.5 Lazy Learners
(or Learning from Your Neighbors)9.6 Other Classification
Methods9.7 Additional Topics Regarding Classification9.8 Summary9.9
Exercises9.10 Bibliographic Notes
Chapter 10. Cluster Analysis: Basic Concepts and Methods10.1
Cluster Analysis10.2 Partitioning Methods10.3 Hierarchical
Methods10.4 Density-Based Methods10.5 Grid-Based Methods10.6
Evaluation of Clustering10.7 Summary10.8 Exercises10.9
Bibliographic Notes
Chapter 11. Advanced Cluster Analysis11.1 Probabilistic
Model-Based Clustering11.2 Clustering High-Dimensional Data11.3
Clustering Graph and Network Data11.4 Clustering with
Constraints11.5 Summary11.6 Exercises11.7 Bibliographic Notes
Chapter 12. Outlier Detection12.1 Outliers and Outlier
Analysis12.2 Outlier Detection Methods12.3 Statistical
Approaches12.4 Proximity-Based Approaches12.5 Clustering-Based
Approaches12.6 Classification-Based Approaches12.7 Mining
Contextual and Collective Outliers12.8 Outlier Detection in
High-Dimensional Data12.9 Summary12.10 Exercises12.11 Bibliographic
Notes
Chapter 13. Data Mining Trends and Research Frontiers13.1 Mining
Complex Data Types13.2 Other Methodologies of Data Mining13.3 Data
Mining Applications13.4 Data Mining and Society13.5 Data Mining
Trends13.6 Summary13.7 Exercises13.8 Bibliographic Notes
BibliographyIndexFront Cover Data Mining: Concepts and
TechniquesCopyrightDedicationTable of ContentsForewordForeword to
Second EditionPrefaceAcknowledgmentsAbout the AuthorsChapter 1.
Introduction1.1 Why Data Mining?1.2 What Is Data Mining?1.3 What
Kinds of Data Can Be Mined?1.4 What Kinds of Patterns Can Be
Mined?1.5 Which Technologies Are Used?1.6 Which Kinds of
Applications Are Targeted?1.7 Major Issues in Data Mining1.8
Summary1.9 Exercises1.10 Bibliographic Notes
Chapter 2. Getting to Know Your Data2.1 Data Objects and
Attribute Types2.2 Basic Statistical Descriptions of Data2.3 Data
Visualization2.4 Measuring Data Similarity and Dissimilarity2.5
Summary2.6 Exercises2.7 Bibliographic Notes
Chapter 3. Data Preprocessing3.1 Data Preprocessing: An
Overview3.2 Data Cleaning3.3 Data Integration3.4 Data Reduction3.5
Data Transformation and Data Discretization3.6 Summary3.7
Exercises3.8 Bibliographic Notes
Chapter 4. Data Warehousing and Online Analytical Processing4.1
Data Warehouse: Basic Concepts4.2 Data Warehouse Modeling: Data
Cube and OLAP4.3 Data Warehouse Design and Usage4.4 Data Warehouse
Implementation4.5 Data Generalization by Attribute-Oriented
Induction4.6 Summary4.7 Exercises4.8 Bibliographic Notes
Chapter 5. Data Cube Technology5.1 Data Cube Computation:
Preliminary Concepts5.2 Data Cube Computation Methods5.3 Processing
Advanced Kinds of Queries by Exploring Cube Technology5.4
Multidimensional Data Analysis in Cube Space5.5 Summary5.6
Exercises5.7 Bibliographic Notes
Chapter 6. Mining Frequent Patterns, Associations, and
Correlations: Basic Concepts and Methods6.1 Basic Concepts6.2
Frequent Itemset Mining Methods6.3 Which Patterns Are
Interesting?Pattern Evaluation Methods6.4 Summary6.5 Exercises6.6
Bibliographic Notes
Chapter 7. Advanced Pattern Mining7.1 Pattern Mining: A Road
Map7.2 Pattern Mining in Multilevel, Multidimensional Space7.3
Constraint-Based Frequent Pattern Mining7.4 Mining High-Dimensional
Data and Colossal Patterns7.5 Mining Compressed or Approximate
Patterns7.6 Pattern Exploration and Application7.7 Summary7.8
Exercises7.9 Bibliographic Notes
Chapter 8. Classification: Basic Concepts8.1 Basic Concepts8.2
Decision Tree Induction8.3 Bayes Classification Methods8.4
Rule-Based Classification8.5 Model Evaluation and Selection8.6
Techniques to Improve Classification Accuracy8.7 Summary8.8
Exercises8.9 Bibliographic Notes
Chapter 9. Classification: Advanced Methods9.1 Bayesian Belief
Networks9.2 Classification by Backpropagation9.3 Support Vector
Machines9.4 Classification Using Frequent Patterns9.5 Lazy Learners
(or Learning from Your Neighbors)9.6 Other Classification
Methods9.7 Additional Topics Regarding Classification9.8 Summary9.9
Exercises9.10 Bibliographic Notes
Chapter 10. Cluster Analysis: Basic Concepts and Methods10.1
Cluster Analysis10.2 Partitioning Methods10.3 Hierarchical
Methods10.4 Density-Based Methods10.5 Grid-Based Methods10.6
Evaluation of Clustering10.7 Summary10.8 Exercises10.9
Bibliographic Notes
Chapter 11. Advanced Cluster Analysis11.1 Probabilistic
Model-Based Clustering11.2 Clustering High-Dimensional Data11.3
Clustering Graph and Network Data11.4 Clustering with
Constraints11.5 Summary11.6 Exercises11.7 Bibliographic Notes
Chapter 12. Outlier Detection12.1 Outliers and Outlier
Analysis12.2 Outlier Detection Methods12.3 Statistical
Approaches12.4 Proximity-Based Approaches12.5 Clustering-Based
Approaches12.6 Classification-Based Approaches12.7 Mining
Contextual and Collective Outliers12.8 Outlier Detection in
High-Dimensional Data12.9 Summary12.10 Exercises12.11 Bibliographic
Notes
Chapter 13. Data Mining Trends and Research Frontiers13.1 Mining
Complex Data Types13.2 Other Methodologies of Data Mining13.3 Data
Mining Applications13.4 Data Mining and Society13.5 Data Mining
Trends13.6 Summary13.7 Exercises13.8 Bibliographic Notes
BibliographyIndexFront Cover Data Mining: Concepts and
TechniquesCopyrightDedicationTable of ContentsForewordForeword to
Second EditionPrefaceAcknowledgmentsAbout the AuthorsChapter 1.
Introduction1.1 Why Data Mining?1.2 What Is Data Mining?1.3 What
Kinds of Data Can Be Mined?1.4 What Kinds of Patterns Can Be
Mined?1.5 Which Technologies Are Used?1.6 Which Kinds of
Applications Are Targeted?1.7 Major Issues in Data Mining1.8
Summary1.9 Exercises1.10 Bibliographic Notes
Chapter 2. Getting to Know Your Data2.1 Data Objects and
Attribute Types2.2 Basic Statistical Descriptions of Data2.3 Data
Visualization2.4 Measuring Data Similarity and Dissimilarity2.5
Summary2.6 Exercises2.7 Bibliographic Notes
Chapter 3. Data Preprocessing3.1 Data Preprocessing: An
Overview3.2 Data Cleaning3.3 Data Integration3.4 Data Reduction3.5
Data Transformation and Data Discretization3.6 Summary3.7
Exercises3.8 Bibliographic Notes
Chapter 4. Data Warehousing and Online Analytical Processing4.1
Data Warehouse: Basic Concepts4.2 Data Warehouse Modeling: Data
Cube and OLAP4.3 Data Warehouse Design and Usage4.4 Data Warehouse
Implementation4.5 Data Generalization by Attribute-Oriented
Induction4.6 Summary4.7 Exercises4.8 Bibliographic Notes
Chapter 5. Data Cube Technology5.1 Data Cube Computation:
Preliminary Concepts5.2 Data Cube Computation Methods5.3 Processing
Advanced Kinds of Queries by Exploring Cube Technology5.4
Multidimensional Data Analysis in Cube Space5.5 Summary5.6
Exercises5.7 Bibliographic Notes
Chapter 6. Mining Frequent Patterns, Associations, and
Correlations: Basic Concepts and Methods6.1 Basic Concepts6.2
Frequent Itemset Mining Methods6.3 Which Patterns Are
Interesting?Pattern Evaluation Methods6.4 Summary6.5 Exercises6.6
Bibliographic Notes
Chapter 7. Advanced Pattern Mining7.1 Pattern Mining: A Road
Map7.2 Pattern Mining in Multilevel, Multidimensional Space7.3
Constraint-Based Frequent Pattern Mining7.4 Mining High-Dimensional
Data and Colossal Patterns7.5 Mining Compressed or Approximate
Patterns7.6 Pattern Exploration and Application7.7 Summary7.8
Exercises7.9 Bibliographic Notes
Chapter 8. Classification: Basic Concepts8.1 Basic Concepts8.2
Decision Tree Induction8.3 Bayes Classification Methods8.4
Rule-Based Classification8.5 Model Evaluation and Selection8.6
Techniques to Improve Classification Accuracy8.7 Summary8.8
Exercises8.9 Bibliographic Notes
Chapter 9. Classification: Advanced Methods9.1 Bayesian Belief
Networks9.2 Classification by Backpropagation9.3 Support Vector
Machines9.4 Classification Using Frequent Patterns9.5 Lazy Learners
(or Learning from Your Neighbors)9.6 Other Classification
Methods9.7 Additional Topics Regarding Classification9.8 Summary9.9
Exercises9.10 Bibliographic Notes
Chapter 10. Cluster Analysis: Basic Concepts and Methods10.1
Cluster Analysis10.2 Partitioning Methods10.3 Hierarchical
Methods10.4 Density-Based Methods10.5 Grid-Based Methods10.6
Evaluation of Clustering10.7 Summary10.8 Exercises10.9
Bibliographic Notes
Chapter 11. Advanced Cluster Analysis11.1 Probabilistic
Model-Based Clustering11.2 Clustering High-Dimensional Data11.3
Clustering Graph and Network Data11.4 Clustering with
Constraints11.5 Summary11.6 Exercises11.7 Bibliographic Notes
Chapter 12. Outlier Detection12.1 Outliers and Outlier
Analysis12.2 Outlier Detection Methods12.3 Statistical
Approaches12.4 Proximity-Based Approaches12.5 Clustering-Based
Approaches12.6 Classification-Based Approaches12.7 Mining
Contextual and Collective Outliers12.8 Outlier Detection in
High-Dimensional Data12.9 Summary12.10 Exercises12.11 Bibliographic
Notes
Chapter 13. Data Mining Trends and Research Frontiers13.1 Mining
Complex Data Types13.2 Other Methodologies of Data Mining13.3 Data
Mining Applications13.4 Data Mining and Society13.5 Data Mining
Trends13.6 Summary13.7 Exercises13.8 Bibliographic Notes
BibliographyIndexFront Cover Data Mining: Concepts and
TechniquesCopyrightDedicationTable of ContentsForewordForeword to
Second EditionPrefaceAcknowledgmentsAbout the AuthorsChapter 1.
Introduction1.1 Why Data Mining?1.2 What Is Data Mining?1.3 What
Kinds of Data Can Be Mined?1.4 What Kinds of Patterns Can Be
Mined?1.5 Which Technologies Are Used?1.6 Which Kinds of
Applications Are Targeted?1.7 Major Issues in Data Mining1.8
Summary1.9 Exercises1.10 Bibliographic Notes
Chapter 2. Getting to Know Your Data2.1 Data Objects and
Attribute Types2.2 Basic Statistical Descriptions of Data2.3 Data
Visualization2.4 Measuring Data Similarity and Dissimilarity2.5
Summary2.6 Exercises2.7 Bibliographic Notes
Chapter 3. Data Preprocessing3.1 Data Preprocessing: An
Overview3.2 Data Cleaning3.3 Data Integration3.4 Data Reduction3.5
Data Transformation and Data Discretization3.6 Summary3.7
Exercises3.8 Bibliographic Notes
Chapter 4. Data Warehousing and Online Analytical Processing4.1
Data Warehouse: Basic Concepts4.2 Data Warehouse Modeling: Data
Cube and OLAP4.3 Data Warehouse Design and Usage4.4 Data Warehouse
Implementation4.5 Data Generalization by Attribute-Oriented
Induction4.6 Summary4.7 Exercises4.8 Bibliographic Notes
Chapter 5. Data Cube Technology5.1 Data Cube Computation:
Preliminary Concepts5.2 Data Cube Computation Methods5.3 Processing
Advanced Kinds of Queries by Exploring Cube Technology5.4
Multidimensional Data Analysis in Cube Space5.5 Summary5.6
Exercises5.7 Bibliographic Notes
Chapter 6. Mining Frequent Patterns, Associations, and
Correlations: Basic Concepts and Methods6.1 Basic Concepts6.2
Frequent Itemset Mining Methods6.3 Which Patterns Are
Interesting?Pattern Evaluation Methods6.4 Summary6.5 Exercises6.6
Bibliographic Notes
Chapter 7. Advanced Pattern Mining7.1 Pattern Mining: A Road
Map7.2 Pattern Mining in Multilevel, Multidimensional Space7.3
Constraint-Based Frequent Pattern Mining7.4 Mining High-Dimensional
Data and Colossal Patterns7.5 Mining Compressed or Approximate
Patterns7.6 Pattern Exploration and Application7.7 Summary7.8
Exercises7.9 Bibliographic Notes
Chapter 8. Classification: Basic Concepts8.1 Basic Concepts8.2
Decision Tree Induction8.3 Bayes Classification Methods8.4
Rule-Based Classification8.5 Model Evaluation and Selection8.6
Techniques to Improve Classification Accuracy8.7 Summary8.8
Exercises8.9 Bibliographic Notes
Chapter 9. Classification: Advanced Methods9.1 Bayesian Belief
Networks9.2 Classification by Backpropagation9.3 Support Vector
Machines9.4 Classification Using Frequent Patterns9.5 Lazy Learners
(or Learning from Your Neighbors)9.6 Other Classification
Methods9.7 Additional Topics Regarding Classification9.8 Summary9.9
Exercises9.10 Bibliographic Notes
Chapter 10. Cluster Analysis: Basic Concepts and Methods10.1
Cluster Analysis10.2 Partitioning Methods10.3 Hierarchical
Methods10.4 Density-Based Methods10.5 Grid-Based Methods10.6
Evaluation of Clustering10.7 Summary10.8 Exercises10.9
Bibliographic Notes
Chapter 11. Advanced Cluster Analysis11.1 Probabilistic
Model-Based Clustering11.2 Clustering High-Dimensional Data11.3
Clustering Graph and Network Data11.4 Clustering with
Constraints11.5 Summary11.6 Exercises11.7 Bibliographic Notes
Chapter 12. Outlier Detection12.1 Outliers and Outlier
Analysis12.2 Outlier Detection Methods12.3 Statistical
Approaches12.4 Proximity-Based Approaches12.5 Clustering-Based
Approaches12.6 Classification-Based Approaches12.7 Mining
Contextual and Collective Outliers12.8 Outlier Detection in
High-Dimensional Data12.9 Summary12.10 Exercises12.11 Bibliographic
Notes
Chapter 13. Data Mining Trends and Research Frontiers13.1 Mining
Complex Data Types13.2 Other Methodologies of Data Mining13.3 Data
Mining Applications13.4 Data Mining and Society13.5 Data Mining
Trends13.6 Summary13.7 Exercises13.8 Bibliographic Notes
BibliographyIndexFront Cover Data Mining: Concepts and
TechniquesCopyrightDedicationTable of ContentsForewordForeword to
Second EditionPrefaceAcknowledgmentsAbout the AuthorsChapter 1.
Introduction1.1 Why Data Mining?1.2 What Is Data Mining?1.3 What
Kinds of Data Can Be Mined?1.4 What Kinds of Patterns Can Be
Mined?1.5 Which Technologies Are Used?1.6 Which Kinds of
Applications Are Targeted?1.7 Major Issues in Data Mining1.8
Summary1.9 Exercises1.10 Bibliographic Notes
Chapter 2. Getting to Know Your Data2.1 Data Objects and
Attribute Types2.2 Basic Statistical Descriptions of Data2.3 Data
Visualization2.4 Measuring Data Similarity and Dissimilarity2.5
Summary2.6 Exercises2.7 Bibliographic Notes
Chapter 3. Data Preprocessing3.1 Data Preprocessing: An
Overview3.2 Data Cleaning3.3 Data Integration3.4 Data Reduction3.5
Data Transformation and Data Discretization3.6 Summary3.7
Exercises3.8 Bibliographic Notes
Chapter 4. Data Warehousing and Online Analytical Processing4.1
Data Warehouse: Basic Concepts4.2 Data Warehouse Modeling: Data
Cube and OLAP4.3 Data Warehouse Design and Usage4.4 Data Warehouse
Implementation4.5 Data Generalization by Attribute-Oriented
Induction4.6 Summary4.7 Exercises4.8 Bibliographic Notes
Chapter 5. Data Cube Technology5.1 Data Cube Computation:
Preliminary Concepts5.2 Data Cube Computation Methods5.3 Processing
Advanced Kinds of Queries by Exploring Cube Technology5.4
Multidimensional Data Analysis in Cube Space5.5 Summary5.6
Exercises5.7 Bibliographic Notes
Chapter 6. Mining Frequent Patterns, Associations, and
Correlations: Basic Concepts and Methods6.1 Basic Concepts6.2
Frequent Itemset Mining Methods6.3 Which Patterns Are
Interesting?Pattern Evaluation Methods6.4 Summary6.5 Exercises6.6
Bibliographic Notes
Chapter 7. Advanced Pattern Mining7.1 Pattern Mining: A Road
Map7.2 Pattern Mining in Multilevel, Multidimensional Space7.3
Constraint-Based Frequent Pattern Mining7.4 Mining High-Dimensional
Data and Colossal Patterns7.5 Mining Compressed or Approximate
Patterns7.6 Pattern Exploration and Application7.7 Summary7.8
Exercises7.9 Bibliographic Notes
Chapter 8. Classification: Basic Concepts8.1 Basic Concepts8.2
Decision Tree Induction8.3 Bayes Classification Methods8.4
Rule-Based Classification8.5 Model Evaluation and Selection8.6
Techniques to Improve Classification Accuracy8.7 Summary8.8
Exercises8.9 Bibliographic Notes
Chapter 9. Classification: Advanced Methods9.1 Bayesian Belief
Networks9.2 Classification by Backpropagation9.3 Support Vector
Machines9.4 Classification Using Frequent Patterns9.5 Lazy Learners
(or Learning from Your Neighbors)9.6 Other Classification
Methods9.7 Additional Topics Regarding Classification9.8 Summary9.9
Exercises9.10 Bibliographic Notes
Chapter 10. Cluster Analysis: Basic Concepts and Methods10.1
Cluster Analysis10.2 Partitioning Methods10.3 Hierarchical
Methods10.4 Density-Based Methods10.5 Grid-Based Methods10.6
Evaluation of Clustering10.7 Summary10.8 Exercises10.9
Bibliographic Notes
Chapter 11. Advanced Cluster Analysis11.1 Probabilistic
Model-Based Clustering11.2 Clustering High-Dimensional Data11.3
Clustering Graph and Network Data11.4 Clustering with
Constraints11.5 Summary11.6 Exercises11.7 Bibliographic Notes
Chapter 12. Outlier Detection12.1 Outliers and Outlier
Analysis12.2 Outlier Detection Methods12.3 Statistical
Approaches12.4 Proximity-Based Approaches12.5 Clustering-Based
Approaches12.6 Classification-Based Approaches12.7 Mining
Contextual and Collective Outliers12.8 Outlier Detection in
High-Dimensional Data12.9 Summary12.10 Exercises12.11 Bibliographic
Notes
Chapter 13. Data Mining Trends and Research Frontiers13.1 Mining
Complex Data Types13.2 Other Methodologies of Data Mining13.3 Data
Mining Applications13.4 Data Mining and Society13.5 Data Mining
Trends13.6 Summary13.7 Exercises13.8 Bibliographic Notes
BibliographyIndexFront Cover Data Mining: Concepts and
TechniquesCopyrightDedicationTable of ContentsForewordForeword to
Second EditionPrefaceAcknowledgmentsAbout the AuthorsChapter 1.
Introduction1.1 Why Data Mining?1.2 What Is Data Mining?1.3 What
Kinds of Data Can Be Mined?1.4 What Kinds of Patterns Can Be
Mined?1.5 Which Technologies Are Used?1.6 Which Kinds of
Applications Are Targeted?1.7 Major Issues in Data Mining1.8
Summary1.9 Exercises1.10 Bibliographic Notes
Chapter 2. Getting to Know Your Data2.1 Data Objects and
Attribute Types2.2 Basic Statistical Descriptions of Data2.3 Data
Visualization2.4 Measuring Data Similarity and Dissimilarity2.5
Summary2.6 Exercises2.7 Bibliographic Notes
Chapter 3. Data Preprocessing3.1 Data Preprocessing: An
Overview3.2 Data Cleaning3.3 Data Integration3.4 Data Reduction3.5
Data Transformation and Data Discretization3.6 Summary3.7
Exercises3.8 Bibliographic Notes
Chapter 4. Data Warehousing and Online Analytical Processing4.1
Data Warehouse: Basic Concepts4.2 Data Warehouse Modeling: Data
Cube and OLAP4.3 Data Warehouse Design and Usage4.4 Data Warehouse
Implementation4.5 Data Generalization by Attribute-Oriented
Induction4.6 Summary4.7 Exercises4.8 Bibliographic Notes
Chapter 5. Data Cube Technology5.1 Data Cube Computation:
Preliminary Concepts5.2 Data Cube Computation Methods5.3 Processing
Advanced Kinds of Queries by Exploring Cube Technology5.4
Multidimensional Data Analysis in Cube Space5.5 Summary5.6
Exercises5.7 Bibliographic Notes
Chapter 6. Mining Frequent Patterns, Associations, and
Correlations: Basic Concepts and Methods6.1 Basic Concepts6.2
Frequent Itemset Mining Methods6.3 Which Patterns Are
Interesting?Pattern Evaluation Methods6.4 Summary6.5 Exercises6.6
Bibliographic Notes
Chapter 7. Advanced Pattern Mining7.1 Pattern Mining: A Road
Map7.2 Pattern Mining in Multilevel, Multidimensional Space7.3
Constraint-Based Frequent Pattern Mining7.4 Mining High-Dimensional
Data and Colossal Patterns7.5 Mining Compressed or Approximate
Patterns7.6 Pattern Exploration and Application7.7 Summary7.8
Exercises7.9 Bibliographic Notes
Chapter 8. Classification: Basic Concepts8.1 Basic Concepts8.2
Decision Tree Induction8.3 Bayes Classification Methods8.4
Rule-Based Classification8.5 Model Evaluation and Selection8.6
Techniques to Improve Classification Accuracy8.7 Summary8.8
Exercises8.9 Bibliographic Notes
Chapter 9. Classification: Advanced Methods9.1 Bayesian Belief
Networks9.2 Classification by Backpropagation9.3 Support Vector
Machines9.4 Classification Using Frequent Patterns9.5 Lazy Learners
(or Learning from Your Neighbors)9.6 Other Classification
Methods9.7 Additional Topics Regarding Classification9.8 Summary9.9
Exercises9.10 Bibliographic Notes
Chapter 10. Cluster Analysis: Basic Concepts and Methods10.1
Cluster Analysis10.2 Partitioning Methods10.3 Hierarchical
Methods10.4 Density-Based Methods10.5 Grid-Based Methods10.6
Evaluation of Clustering10.7 Summary10.8 Exercises10.9
Bibliographic Notes
Chapter 11. Advanced Cluster Analysis11.1 Probabilistic
Model-Based Clustering11.2 Clustering High-Dimensional Data11.3
Clustering Graph and Network Data11.4 Clustering with
Constraints11.5 Summary11.6 Exercises11.7 Bibliographic Notes
Chapter 12. Outlier Detection12.1 Outliers and Outlier
Analysis12.2 Outlier Detection Methods12.3 Statistical
Approaches12.4 Proximity-Based Approaches12.5 Clustering-Based
Approaches12.6 Classification-Based Approaches12.7 Mining
Contextual and Collective Outliers12.8 Outlier Detection in
High-Dimensional Data12.9 Summary12.10 Exercises12.11 Bibliographic
Notes
Chapter 13. Data Mining Trends and Research Frontiers13.1 Mining
Complex Data Types13.2 Other Methodologies of Data Mining13.3 Data
Mining Applications13.4 Data Mining and Society13.5 Data Mining
Trends13.6 Summary13.7 Exercises13.8 Bibliographic Notes
BibliographyIndexFront Cover Data Mining: Concepts and
TechniquesCopyrightDedicationTable of ContentsForewordForeword to
Second EditionPrefaceAcknowledgmentsAbout the AuthorsChapter 1.
Introduction1.1 Why Data Mining?1.2 What Is Data Mining?1.3 What
Kinds of Data Can Be Mined?1.4 What Kinds of Patterns Can Be
Mined?1.5 Which Technologies Are Used?1.6 Which Kinds of
Applications Are Targeted?1.7 Major Issues in Data Mining1.8
Summary1.9 Exercises1.10 Bibliographic Notes
Chapter 2. Getting to Know Your Data2.1 Data Objects and
Attribute Types2.2 Basic Statistical Descriptions of Data2.3 Data
Visualization2.4 Measuring Data Similarity and Dissimilarity2.5
Summary2.6 Exercises2.7 Bibliographic Notes
Chapter 3. Data Preprocessing3.1 Data Preprocessing: An
Overview3.2 Data Cleaning3.3 Data Integration3.4 Data Reduction3.5
Data Transformation and Data Discretization3.6 Summary3.7
Exercises3.8 Bibliographic Notes
Chapter 4. Data Warehousing and Online Analytical Processing4.1
Data Warehouse: Basic Concepts4.2 Data Warehouse Modeling: Data
Cube and OLAP4.3 Data Warehouse Design and Usage4.4 Data Warehouse
Implementation4.5 Data Generalization by Attribute-Oriented
Induction4.6 Summary4.7 Exercises4.8 Bibliographic Notes
Chapter 5. Data Cube Technology5.1 Data Cube Computation:
Preliminary Concepts5.2 Data Cube Computation Methods5.3 Processing
Advanced Kinds of Queries by Exploring Cube Technology5.4
Multidimensional Data Analysis in Cube Space5.5 Summary5.6
Exercises5.7 Bibliographic Notes
Chapter 6. Mining Frequent Patterns, Associations, and
Correlations: Basic Concepts and Methods6.1 Basic Concepts6.2
Frequent Itemset Mining Methods6.3 Which Patterns Are
Interesting?Pattern Evaluation Methods6.4 Summary6.5 Exercises6.6
Bibliographic Notes
Chapter 7. Advanced Pattern Mining7.1 Pattern Mining: A Road
Map7.2 Pattern Mining in Multilevel, Multidimensional Space7.3
Constraint-Based Frequent Pattern Mining7.4 Mining High-Dimensional
Data and Colossal Patterns7.5 Mining Compressed or Approximate
Patterns7.6 Pattern Exploration and Application7.7 Summary7.8
Exercises7.9 Bibliographic Notes
Chapter 8. Classification: Basic Concepts8.1 Basic Concepts8.2
Decision Tree Induction8.3 Bayes Classification Methods8.4
Rule-Based Classification8.5 Model Evaluation and Selection8.6
Techniques to Improve Classification Accuracy8.7 Summary8.8
Exercises8.9 Bibliographic Notes
Chapter 9. Classification: Advanced Methods9.1 Bayesian Belief
Networks9.2 Classification by Backpropagation9.3 Support Vector
Machines9.4 Classification Using Frequent Patterns9.5 Lazy Learners
(or Learning from Your Neighbors)9.6 Other Classification
Methods9.7 Additional Topics Regarding Classification9.8 Summary9.9
Exercises9.10 Bibliographic Notes
Chapter 10. Cluster Analysis: Basic Concepts and Methods10.1
Cluster Analysis10.2 Partitioning Methods10.3 Hierarchical
Methods10.4 Density-Based Methods10.5 Grid-Based Methods10.6
Evaluation of Clustering10.7 Summary10.8 Exercises10.9
Bibliographic Notes
Chapter 11. Advanced Cluster Analysis11.1 Probabilistic
Model-Based Clustering11.2 Clustering High-Dimensional Data11.3
Clustering Graph and Network Data11.4 Clustering with
Constraints11.5 Summary11.6 Exercises11.7 Bibliographic Notes
Chapter 12. Outlier Detection12.1 Outliers and Outlier
Analysis12.2 Outlier Detection Methods12.3 Statistical
Approaches12.4 Proximity-Based Approaches12.5 Clustering-Based
Approaches12.6 Classification-Based Approaches12.7 Mining
Contextual and Collective Outliers12.8 Outlier Detection in
High-Dimensional Data12.9 Summary12.10 Exercises12.11 Bibliographic
Notes
Chapter 13. Data Mining Trends and Research Frontiers13.1 Mining
Complex Data Types13.2 Other Methodologies of Data Mining13.3 Data
Mining Applications13.4 Data Mining and Society13.5 Data Mining
Trends13.6 Summary13.7 Exercises13.8 Bibliographic Notes
BibliographyIndex