Data mining: concepts and techniques
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam [u.a.]
Elsevier
2006
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | The Morgan Kaufmann series in data management systems
|
| Schlagworte: | |
| Online-Zugang: | kostenfrei Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. 703-743 |
| Beschreibung: | XXVIII, 770 S. |
| ISBN: | 1558609016 9781558609013 |
Internformat
MARC
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| 100 | 1 | |a Han, Jiawei |d 1949- |e Verfasser |0 (DE-588)137798342 |4 aut | |
| 245 | 1 | 0 | |a Data mining |b concepts and techniques |c Jiawei Han ; Micheline Kamber |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier |c 2006 | |
| 300 | |a XXVIII, 770 S. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a The Morgan Kaufmann series in data management systems | |
| 500 | |a Literaturverz. S. 703-743 | ||
| 650 | 4 | |a Exploration de données (Informatique) | |
| 650 | 4 | |a Veri madenciliği | |
| 650 | 4 | |a Data mining | |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Kamber, Micheline |e Verfasser |4 aut | |
| 856 | 4 | |u http://www.loc.gov/catdir/enhancements/fy0664/2006296324-d.html |y Publisher description |z kostenfrei | |
| 856 | 4 | |m DE-601 |q pdf/application |u http://www.gbv.de/dms/bowker/toc/9781558609013.pdf |3 Inhaltsverzeichnis | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014185035&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-014185035 | ||
Datensatz im Suchindex
| _version_ | 1804134584598659072 |
|---|---|
| adam_text | Contents
Foreword
xix
Preface
xxi
Chapter I Introduction I
I
.
I What Motivated Data Mining? Why Is It Important? I
I
2
So, What Is Data Mining?
5
I
3
Data Mining—On What Kind of Data?
9
1.3.1
Relational Databases
10
1.3.2
Data Warehouses
12
1
.3.3
Transactional Databases 1
4
1
.3.4
Advanced Data and Information Systems and Advanced
Applications 1
5
1
.4
Data Mining Functionalities
—
What Kinds of Patterns Can Be
Mined?
21
1
.4.
1 Concept/Class Description: Characterization and
Discrimination
21
1
.4.2
Mining Frequent Patterns, Associations, and Correlations
23
1
.4.3
Classification and Prediction
24
1
.4.4
Cluster Analysis
25
1
.4.5
Outlier Analysis
26
1
.4.6
Evolution Analysis
27
I
5
Are All of the Patterns Interesting?
27
1
.6
Classification of Data Mining Systems
29
1
.7
Data Mining Task Primitives
ЗІ
1
.8
Integration of a Data Mining System with
a Database or Data Warehouse System
34
1
.9
Major Issues in Data Mining
36
ix
Contents
1.10
Summary
39
Exercises
40
Bibliographic Notes
42
Chapter
2
Data Preprocessing
47
2.
1 Why Preprocess the Data?
48
2.2
Descriptive Data Summarization
5
1
2.2.
1 Measuring the Central Tendency
5
1
2.2.2
Measuring the Dispersion of Data
53
2.2.3
Graphic Displays of Basic Descriptive Data Summaries
56
2.3
Data Cleaning
61
2.3.1
Missing Values
61
2.3.2
Noisy Data
62
2.3.3
Data Cleaning as a Process
65
2.4
Data Integration and Transformation
67
2.4.
1 Data Integration
67
2.4.2
Data Transformation
70
2.5
Data Reduction
72
2.5.
1 Data Cube Aggregation
73
2.5.2
Attribute Subset Selection
75
2.5.3
Dimensionality Reduction
77
2.5.4
Numerosity Reduction
80
2.6
Data Discretization and Concept Hierarchy Generation
86
2.6.
1 Discretization and Concept Hierarchy Generation for
Numerical Data
88
2.6.2
Concept Hierarchy Generation for Categorical Data
94
2.7
Summary
97
Exercises
97
Bibliographic Notes 1
0
1
Chapter
3
Data Warehouse and
OLAP
Technology: An Overview 1
05
3.
1 What Is a Data Warehouse? 1
05
3.1.1
Differences between Operational Database Systems
and Data Warehouses 1
08
3.
1
.2
But, Why Have a Separate Data Warehouse? 1
09
3.2
A Multidimensional Data Model
110
3.2.
1 From Tables and Spreadsheets to Data Cubes I 1
0
3.2.2
Stars, Snowflakes, and Fact Constellations:
Schemas
for Multidimensional Databases I 1
4
3.2.3
Examples for Defining Star; Snowflake,
and Fact Constellation
Schemas
I 1
7
Contents xi
3.2.4
Measures: Their Categorization and Computation I 1
9
3.2.5
Concept Hierarchies 1
2
1
3.2.6 OLAP
Operations in the Multidimensional Data Model 1
23
3.2.7
A Starnet Query Model for Querying
Multidimensional Databases 1
26
3.3
Data Warehouse Architecture 1
27
3.3.
1 Steps for the Design and Construction of Data Warehouses 1
28
3.3.2
A Three-Tier Data Warehouse Architecture 1
30
3.3.3
Data Warehouse Back-End Tools and Utilities 1
34
3.3.4
Metadata Repository 1
34
3.3.5
Types of
OLAP
Servers: ROLAP versus MOLAP
versus HOLAP 1
35
3.4
Data Warehouse Implementation
137
3.4.
1 Efficient Computation of Data Cubes 1
37
3.4.2
Indexing
OLAP
Data
141
3.4.3
Efficient Processing of
OLAP
Queries 1
44
3.5
From Data Warehousing to Data Mining
146
3.5.
1 Data Warehouse Usage 1
46
3.5.2
From On-Line Analytical Processing
to On-Line Analytical Mining 1
48
3.6
Summary 1
50
Exercises
152
Bibliographic Notes 1
54
Chapter
4
Data Cube Computation and Data Generalization 1
57
4.
1 Efficient Methods for Data Cube Computation 1
57
4.
1
.
1 A Road Map for the Materialization of Different Kinds
of Cubes
158
4.
1
.2
Multiway Array Aggregation for Full Cube Computation 1
64
4.
1
.3
BUC:
Computing Iceberg Cubes from the Apex Cuboid
Downward 1
68
4.
1
.4
Star-cubing: Computing Iceberg Cubes Using
a Dynamic Star-tree Structure 1
73
4.
1
.5
Precomputing Shell Fragments for Fast High-Dimensional
OLAP 178
4.
1
.6
Computing Cubes with Complex Iceberg Conditions 1
87
4.2
Further Development of Data Cube and
OLAP
Technology 1
89
4.2.
1 Discovery-Driven Exploration of Data Cubes 1
89
4.2.2
Complex Aggregation at Multiple Granularity:
Multifeature Cubes 1
92
4.2.3
Constrained Gradient Analysis in Data Cubes
195
xii Contents
4.3
Attribute-Oriented Induction
—
An Alternative
Method for Data Generalization and Concept Description 1
98
4.3.
1 Attribute-Oriented Induction for Data Characterization
199
4.3.2
Efficient Implementation of Attribute-Oriented Induction
205
4.3.3
Presentation of the Derived Generalization
206
4.3.4
Mining Class Comparisons: Discriminating between
Different Classes
2
1
0
4.3.5
Class Description: Presentation of Both Characterization
and Comparison
2
1
5
4.4
Summary
2
1
8
Exercises
2
1
9
Bibliographic Notes
223
Chapter
5
Mining Frequent Patterns, Associations, and Correlations
227
5.
1 Basic Concepts and a Road Map
227
5.
1
.
1 Market Basket Analysis: A Motivating Example
228
5.
1
.2
Frequent Itemsets, Closed Itemsets, and Association Rules
230
5.
1
.3
Frequent Pattern Mining: A Road Map
232
5.2
Efficient and Scalable Frequent Itemset Mining Methods
234
5.2.
1 The
Apriori
Algorithm: Finding Frequent Itemsets Using
Candidate Generation
234
5.2.2
Generating Association Rules from Frequent Itemsets
239
5.2.3
Improving the Efficiency of
Apriori
240
5.2.4
Mining Frequent Itemsets without Candidate Generation
242
5.2.5
Mining Frequent Itemsets Using Vertical Data Format
245
5.2.6
Mining Closed Frequent Itemsets
248
5.3
Mining Various Kinds of Association Rules
250
5.3.
1 Mining Multilevel Association Rules
250
5.3.2
Mining Multidimensional Association Rules
from Relational Databases and Data Warehouses
254
5.4
From Association Mining to Correlation Analysis
259
5.4.
1 Strong Rules Are Not Necessarily Interesting: An Example
260
5.4.2
From Association Analysis to Correlation Analysis
261
5.5
Constraint-Based Association Mining
265
5.5.1
Metarule-Guided Mining of Association Rules
266
5.5.2
Constraint Pushing: Mining Guided by Rule Constraints
267
5.6
Summary
272
Exercises
274
Bibliographic Notes
280
Contents xiii
Chapter
6
Classification and Prediction
285
6.
1 What Is Classification? What Is Prediction?
285
6.2
Issues Regarding Classification and Prediction
289
6.2.
1 Preparing the Data for Classification and Prediction
289
6.2.2
Comparing Classification and Prediction Methods
290
6.3
Classification by Decision Tree Induction
291
6.3.
1 Decision Tree Induction
292
6.3.2
Attribute Selection Measures
296
6.3.3
Tree Pruning
304
6.3.4
Scalability and Decision Tree Induction
306
6.4
Bayesian Classification
3
1
0
6.4.
1
Bayes
Theorem
3
1
0
6.4.2
Naïve
Bayesian Classification
ЗІ І
6.4.3
Bayesian Belief Networks
3
1
5
6.4.4
Training Bayesian Belief Networks
3
1
7
6.5
Rule-Based Classification
3
1
8
6.5.1
Using IF-THEN Rules for Classification
319
6.5.2
Rule Extraction from a Decision Tree
32
1
6.5.3
Rule Induction Using a Sequential Covering Algorithm
322
6.6
Classification by Backpropagation
327
6.6.
1 A Multilayer Feed-Forward Neural Network
328
6.6.2
Defining a Network Topology
329
6.6.3
Backpropagation
329
6.6.4
Inside the Black Box: Backpropagation and Interpretability
334
6.7
Support Vector Machines
337
6.7.1
The Case When the Data Are Linearly Separable
337
6.7.2
The Case When the Data Are Linearly Inseparable
342
6.8
Associative Classification: Classification by Association
Rule Analysis
344
6.9
Lazy Learners (or Learning from Your Neighbors)
347
6.9.
1 fc-Nearest-Neighbor Classifiers
348
6.9.2
Case-Based Reasoning
350
6.
1
0
Other Classification Methods
35
1
6.10.1
Genetic Algorithms
351
6.
1
0.2
Rough Set Approach
35
1
6.
1
0.3
Fuzzy Set Approaches
352
6.
1 I Prediction
354
6.
1 I.I Linear Regression
355
6.
1 1
.2
Nonlinear Regression
357
6.
1 1
.3
Other Regression-Based Methods
358
xiv Contents
6.12
Accuracy and Error Measures
359
6.
1
2.
1 Classifier Accuracy Measures
360
6.12.2
Predictor Error Measures
362
6.13
Evaluating the Accuracy of a Classifier or Predictor
363
6.
1
3.
1 Holdout Method and Random
Subsampling
364
6.
1
3.2
Cross-validation
364
6.13.3
Bootstrap
365
6.
1
4
Ensemble Methods
—
Increasing the Accuracy
366
6.14.1
Bagging
366
6.14.2
Boosting
367
6.15
Model Selection
370
6.
1
5.
1 Estimating Confidence Intervals
370
6.15.2
ROC Curves
372
6.
1
6
Summary
373
Exercises
375
Bibliographic Notes
378
Chapter
7
Cluster Analysis
383
7.
1 What Is Cluster Analysis?
383
7.2
Types of Data in Cluster Analysis
386
7.2.1
Interval-Scaled Variables
387
7.2.2
Binary Variables
389
7.2.3
Categorical, Ordinal, and Ratio-Scaled Variables
392
7.2.4
Variables of Mixed Types
395
7.2.5
Vector Objects
397
7.3
A Categorization of Major Clustering Methods
398
7.4
Partitioning Methods
401
7.4.
1 Classical Partitioning Methods: /t-Means and ¿-Medoids
402
7.4.2
Partitioning Methods in Large Databases: From
/t-Medoids to CLARANS
407
7.5
Hierarchical Methods
408
7.5.
1 Agglomerative and Divisive Hierarchical Clustering
408
7.5.2
BIRCH: Balanced Iterative Reducing and Clustering
Using Hierarchies
4
1
2
7.5.3
ROCK: A Hierarchical Clustering Algorithm for
Categorical Attributes
414
7.5.4
Chameleon: A Hierarchical Clustering Algorithm
Using Dynamic Modeling
4
1
6
7 6
Density-Based Methods
4
1
8
7.6.
1 DBSCAN: A Density-Based Clustering Method Based on
Connected Regions with Sufficiently High Density
4
1
8
Contents xv
7.6.2
OPTICS: Ordering Points to Identify the Clustering
Structure
420
7.6.3
DENCLUE: Clustering Based on Density
Distribution Functions
422
7.7
Grid-Based Methods
424
7.7.
1 STING: STatistical INformation Grid
425
7.7.2
WaveCluster: Clustering Using Wavelet Transformation
427
7.8
Model-Based Clustering Methods
429
7.8.
1 Expectation-Maximization
429
7.8.2
Conceptual Clustering
43
1
7.8.3
Neural Network Approach
433
7.9
Clustering High-Dimensional Data
434
7.9.
1 CLIQUE: A Dimension-Growth Subspace Clustering Method
436
7.9.2
PROCLUS: A Dimension-Reduction Subspace Clustering
Method
439
7.9.3
Frequent Pattern-Based Clustering Methods
440
7.
1
0
Constraint-Based Cluster Analysis
444
7.
1
0.
1 Clustering with Obstacle Objects
446
7.10.2
User-Constrained Cluster Analysis
448
7.
1
0.3
Semi-Supervised Cluster Analysis
449
7
I I Outlier Analysis
45
1
7.
1 I
.
I Statistical Distribution-Based Outlier Detection
452
7.
1 1
.2
Distance-Based Outlier Detection
454
7.
1 1
.3
Density-Based Local Outlier Detection
455
7.
1 1
.4
Deviation-Based Outlier Detection
458
7.
1
2
Summary
460
Exercises
46
1
Bibliographic Notes
464
Chapter
8
Mining Stream, Time-Series, and Sequence Data
467
8.
1 Mining Data Streams
468
8.
1
.
1 Methodologies for Stream Data Processing and
Stream Data Systems
469
8.
1
.2
Stream
OLAP
and Stream Data Cubes
474
8.
1
.3
Frequent-Pattern Mining in Data Streams
479
8.
1
.4
Classification of Dynamic Data Streams
48
1
8.
1
.5
Clustering Evolving Data Streams
486
8.2
Mining Time-Series Data
489
8.2.
1 Trend Analysis
490
8.2.2
Similarity Search in Time-Series Analysis
493
xvi Contents
8.3 Mining
Sequence Patterns in Transactional Databases
498
8.3.
1 Sequential Pattern
Mining:
Concepts and
Primitives 498
8.3.2
Scalable Methods for
Mining
Sequential Patterns
500
8.3.3
Constraint-Based Mining of Sequential Patterns
509
8.3.4
Periodicity Analysis for Time-Related Sequence Data
5
1
2
8.4
Mining Sequence Patterns in Biological Data
5
1
3
8.4.
1 Alignment of Biological Sequences
5
1
4
8.4.2
Hidden Markov Model for Biological Sequence Analysis
5
1
8
8.5
Summary
527
Exercises
528
Bibliographic Notes
53
1
Chapter
9
Graph Mining, Social Network Analysis, and
Multirelational
Data Mining
535
9.1
Graph Mining
535
9.
1
.
1 Methods for Mining Frequent Subgraphs
536
9.
1
.2
Mining Variant and Constrained Substructure Patterns
545
9.
1
.3
Applications: Graph Indexing, Similarity Search, Classification,
and Clustering
55
1
9 2
Social Network Analysis
556
9.2.
1 What Is a Social Network?
556
9.2.2
Characteristics of Social Networks
557
9.2.3
Link Mining: Tasks and Challenges
56
1
9.2.4
Mining on Social Networks
565
9 3 Multirelational Data
Mining
57
1
9.3.
1 What Is
Multirelational Data
Mining?
571
9.3.2
ILP Approach to
Multirelational
Classification
573
9.3.3
Tuple ID Propagation
575
9.3.4 Multirelational
Classification Using Tuple ID Propagation
577
9.3.5 Multirelational
Clustering with User Guidance
580
9.4
Summary
584
Exercises
586
Bibliographic Notes
587
Chapter 1
0
Mining Object, Spatial, Multimedia, Text, and Web Data
59
1
1
0.
1 Multidimensional Analysis and Descriptive Mining of Complex
Data Objects
591
1
0.
1
.
1 Generalization of Structured Data
592
10.1.2
Aggregation and Approximation in Spatial and Multimedia Data
Generalization
593
Contents xvii
IO.
1.3
Generalization of Object Identifiers and Class/Subclass
Hierarchies
594
10.1.4
Generalization of Class Composition Hierarchies
595
1
0.
1
.5
Construction and Mining of Object Cubes
596
10.1.6
Generalization-Based Mining of Plan Databases by
Divide-and-Conquer
596
1
0.2
Spatial Data Mining
600
10.2.
1 Spatial Data Cube Construction and Spatial
OLAP 601
1
0.2.2
Mining Spatial Association and Co-location Patterns
605
1
0.2.3
Spatial Clustering Methods
606
1
0.2.4
Spatial Classification and Spatial Trend Analysis
606
10.2.5
Mining Raster Databases
607
1
0.3
Multimedia Data Mining
607
1
0.3.
1 Similarity Search in Multimedia Data
608
1
0.3.2
Multidimensional Analysis of Multimedia Data
609
10.3.3
Classification and Prediction Analysis of Multimedia Data
611
10.3.4
Mining Associations in Multimedia Data
612
1
0.3.5
Audio and Video Data Mining
6
1
3
10.4
Text Mining
614
1
0.4.
1 Text Data Analysis and Information Retrieval
6
1
5
10.4.2
Dimensionality Reduction for Text
621
10.4.3
Text Mining Approaches
624
1
0.5
Mining the World Wide Web
628
1
0.5.
1 Mining the Web Page Layout Structure
630
1
0.5.2
Mining the Web s Link Structures to Identify
Authoritative Web Pages
63
1
1
0.5.3
Mining Multimedia Data on the Web
637
10.5.4
Automatic Classification of Web Documents
638
10.5.5
Web Usage Mining
640
1
0.6
Summary
64
1
Exercises
642
Bibliographic Notes
645
Chapter I I Applications and Trends in Data Mining
649
III Data Mining Applications
649
I I.I.I Data Mining for Financial Data Analysis
649
I 1
.
1
.2
Data Mining for the Retail Industry
65
1
I 1
.
1
.3
Data Mining for the Telecommunication Industry
652
I 1
.
1
.4
Data Mining for Biological Data Analysis
654
I 1
.
1
.5
Data Mining in Other Scientific Applications
657
I 1
.
1
.6
Data Mining for Intrusion Detection
658
xviii Contents
I 1
.2
Data Mining System Products and Research Prototypes
660
I 1
.2.
1 How to Choose a Data Mining System
660
I 1
.2.2
Examples of Commercial Data Mining Systems
663
I 1
.3
Additional Themes on Data Mining
665
I 1
.3.
1 Theoretical Foundations of Data Mining
665
I 1
.3.2
Statistical Data Mining
666
I 1
.3.3
Visual and Audio Data Mining
667
I 1
.3.4
Data Mining and Collaborative Filtering
670
I 1
.4
Social Impacts of Data Mining
675
I 1
.4.
1 Ubiquitous and Invisible Data Mining
675
I 1
.4.2
Data Mining, Privacy, and Data Security
678
I 1
.5
Trends in Data Mining
68
1
I
1.6
Summary
684
Exercises
685
Bibliographic Notes
687
Appendix An Introduction to Microsoft s OLE
DB
for
Data Mining
69
1
A. I Model Creation
693
A.2 Model Training
695
A.3 Model Prediction and Browsing
697
Bibliography
703
Index
745
|
| adam_txt |
Contents
Foreword
xix
Preface
xxi
Chapter I Introduction I
I
.
I What Motivated Data Mining? Why Is It Important? I
I
2
So, What Is Data Mining?
5
I
3
Data Mining—On What Kind of Data?
9
1.3.1
Relational Databases
10
1.3.2
Data Warehouses
12
1
.3.3
Transactional Databases 1
4
1
.3.4
Advanced Data and Information Systems and Advanced
Applications 1
5
1
.4
Data Mining Functionalities
—
What Kinds of Patterns Can Be
Mined?
21
1
.4.
1 Concept/Class Description: Characterization and
Discrimination
21
1
.4.2
Mining Frequent Patterns, Associations, and Correlations
23
1
.4.3
Classification and Prediction
24
1
.4.4
Cluster Analysis
25
1
.4.5
Outlier Analysis
26
1
.4.6
Evolution Analysis
27
I
5
Are All of the Patterns Interesting?
27
1
.6
Classification of Data Mining Systems
29
1
.7
Data Mining Task Primitives
ЗІ
1
.8
Integration of a Data Mining System with
a Database or Data Warehouse System
34
1
.9
Major Issues in Data Mining
36
ix
Contents
1.10
Summary
39
Exercises
40
Bibliographic Notes
42
Chapter
2
Data Preprocessing
47
2.
1 Why Preprocess the Data?
48
2.2
Descriptive Data Summarization
5
1
2.2.
1 Measuring the Central Tendency
5
1
2.2.2
Measuring the Dispersion of Data
53
2.2.3
Graphic Displays of Basic Descriptive Data Summaries
56
2.3
Data Cleaning
61
2.3.1
Missing Values
61
2.3.2
Noisy Data
62
2.3.3
Data Cleaning as a Process
65
2.4
Data Integration and Transformation
67
2.4.
1 Data Integration
67
2.4.2
Data Transformation
70
2.5
Data Reduction
72
2.5.
1 Data Cube Aggregation
73
2.5.2
Attribute Subset Selection
75
2.5.3
Dimensionality Reduction
77
2.5.4
Numerosity Reduction
80
2.6
Data Discretization and Concept Hierarchy Generation
86
2.6.
1 Discretization and Concept Hierarchy Generation for
Numerical Data
88
2.6.2
Concept Hierarchy Generation for Categorical Data
94
2.7
Summary
97
Exercises
97
Bibliographic Notes 1
0
1
Chapter
3
Data Warehouse and
OLAP
Technology: An Overview 1
05
3.
1 What Is a Data Warehouse? 1
05
3.1.1
Differences between Operational Database Systems
and Data Warehouses 1
08
3.
1
.2
But, Why Have a Separate Data Warehouse? 1
09
3.2
A Multidimensional Data Model
110
3.2.
1 From Tables and Spreadsheets to Data Cubes I 1
0
3.2.2
Stars, Snowflakes, and Fact Constellations:
Schemas
for Multidimensional Databases I 1
4
3.2.3
Examples for Defining Star; Snowflake,
and Fact Constellation
Schemas
I 1
7
Contents xi
3.2.4
Measures: Their Categorization and Computation I 1
9
3.2.5
Concept Hierarchies 1
2
1
3.2.6 OLAP
Operations in the Multidimensional Data Model 1
23
3.2.7
A Starnet Query Model for Querying
Multidimensional Databases 1
26
3.3
Data Warehouse Architecture 1
27
3.3.
1 Steps for the Design and Construction of Data Warehouses 1
28
3.3.2
A Three-Tier Data Warehouse Architecture 1
30
3.3.3
Data Warehouse Back-End Tools and Utilities 1
34
3.3.4
Metadata Repository 1
34
3.3.5
Types of
OLAP
Servers: ROLAP versus MOLAP
versus HOLAP 1
35
3.4
Data Warehouse Implementation
137
3.4.
1 Efficient Computation of Data Cubes 1
37
3.4.2
Indexing
OLAP
Data
141
3.4.3
Efficient Processing of
OLAP
Queries 1
44
3.5
From Data Warehousing to Data Mining
146
3.5.
1 Data Warehouse Usage 1
46
3.5.2
From On-Line Analytical Processing
to On-Line Analytical Mining 1
48
3.6
Summary 1
50
Exercises
152
Bibliographic Notes 1
54
Chapter
4
Data Cube Computation and Data Generalization 1
57
4.
1 Efficient Methods for Data Cube Computation 1
57
4.
1
.
1 A Road Map for the Materialization of Different Kinds
of Cubes
158
4.
1
.2
Multiway Array Aggregation for Full Cube Computation 1
64
4.
1
.3
BUC:
Computing Iceberg Cubes from the Apex Cuboid
Downward 1
68
4.
1
.4
Star-cubing: Computing Iceberg Cubes Using
a Dynamic Star-tree Structure 1
73
4.
1
.5
Precomputing Shell Fragments for Fast High-Dimensional
OLAP 178
4.
1
.6
Computing Cubes with Complex Iceberg Conditions 1
87
4.2
Further Development of Data Cube and
OLAP
Technology 1
89
4.2.
1 Discovery-Driven Exploration of Data Cubes 1
89
4.2.2
Complex Aggregation at Multiple Granularity:
Multifeature Cubes 1
92
4.2.3
Constrained Gradient Analysis in Data Cubes
195
xii Contents
4.3
Attribute-Oriented Induction
—
An Alternative
Method for Data Generalization and Concept Description 1
98
4.3.
1 Attribute-Oriented Induction for Data Characterization
199
4.3.2
Efficient Implementation of Attribute-Oriented Induction
205
4.3.3
Presentation of the Derived Generalization
206
4.3.4
Mining Class Comparisons: Discriminating between
Different Classes
2
1
0
4.3.5
Class Description: Presentation of Both Characterization
and Comparison
2
1
5
4.4
Summary
2
1
8
Exercises
2
1
9
Bibliographic Notes
223
Chapter
5
Mining Frequent Patterns, Associations, and Correlations
227
5.
1 Basic Concepts and a Road Map
227
5.
1
.
1 Market Basket Analysis: A Motivating Example
228
5.
1
.2
Frequent Itemsets, Closed Itemsets, and Association Rules
230
5.
1
.3
Frequent Pattern Mining: A Road Map
232
5.2
Efficient and Scalable Frequent Itemset Mining Methods
234
5.2.
1 The
Apriori
Algorithm: Finding Frequent Itemsets Using
Candidate Generation
234
5.2.2
Generating Association Rules from Frequent Itemsets
239
5.2.3
Improving the Efficiency of
Apriori
240
5.2.4
Mining Frequent Itemsets without Candidate Generation
242
5.2.5
Mining Frequent Itemsets Using Vertical Data Format
245
5.2.6
Mining Closed Frequent Itemsets
248
5.3
Mining Various Kinds of Association Rules
250
5.3.
1 Mining Multilevel Association Rules
250
5.3.2
Mining Multidimensional Association Rules
from Relational Databases and Data Warehouses
254
5.4
From Association Mining to Correlation Analysis
259
5.4.
1 Strong Rules Are Not Necessarily Interesting: An Example
260
5.4.2
From Association Analysis to Correlation Analysis
261
5.5
Constraint-Based Association Mining
265
5.5.1
Metarule-Guided Mining of Association Rules
266
5.5.2
Constraint Pushing: Mining Guided by Rule Constraints
267
5.6
Summary
272
Exercises
274
Bibliographic Notes
280
Contents xiii
Chapter
6
Classification and Prediction
285
6.
1 What Is Classification? What Is Prediction?
285
6.2
Issues Regarding Classification and Prediction
289
6.2.
1 Preparing the Data for Classification and Prediction
289
6.2.2
Comparing Classification and Prediction Methods
290
6.3
Classification by Decision Tree Induction
291
6.3.
1 Decision Tree Induction
292
6.3.2
Attribute Selection Measures
296
6.3.3
Tree Pruning
304
6.3.4
Scalability and Decision Tree Induction
306
6.4
Bayesian Classification
3
1
0
6.4.
1
Bayes'
Theorem
3
1
0
6.4.2
Naïve
Bayesian Classification
ЗІ І
6.4.3
Bayesian Belief Networks
3
1
5
6.4.4
Training Bayesian Belief Networks
3
1
7
6.5
Rule-Based Classification
3
1
8
6.5.1
Using IF-THEN Rules for Classification
319
6.5.2
Rule Extraction from a Decision Tree
32
1
6.5.3
Rule Induction Using a Sequential Covering Algorithm
322
6.6
Classification by Backpropagation
327
6.6.
1 A Multilayer Feed-Forward Neural Network
328
6.6.2
Defining a Network Topology
329
6.6.3
Backpropagation
329
6.6.4
Inside the Black Box: Backpropagation and Interpretability
334
6.7
Support Vector Machines
337
6.7.1
The Case When the Data Are Linearly Separable
337
6.7.2
The Case When the Data Are Linearly Inseparable
342
6.8
Associative Classification: Classification by Association
Rule Analysis
344
6.9
Lazy Learners (or Learning from Your Neighbors)
347
6.9.
1 fc-Nearest-Neighbor Classifiers
348
6.9.2
Case-Based Reasoning
350
6.
1
0
Other Classification Methods
35
1
6.10.1
Genetic Algorithms
351
6.
1
0.2
Rough Set Approach
35
1
6.
1
0.3
Fuzzy Set Approaches
352
6.
1 I Prediction
354
6.
1 I.I Linear Regression
355
6.
1 1
.2
Nonlinear Regression
357
6.
1 1
.3
Other Regression-Based Methods
358
xiv Contents
6.12
Accuracy and Error Measures
359
6.
1
2.
1 Classifier Accuracy Measures
360
6.12.2
Predictor Error Measures
362
6.13
Evaluating the Accuracy of a Classifier or Predictor
363
6.
1
3.
1 Holdout Method and Random
Subsampling
364
6.
1
3.2
Cross-validation
364
6.13.3
Bootstrap
365
6.
1
4
Ensemble Methods
—
Increasing the Accuracy
366
6.14.1
Bagging
366
6.14.2
Boosting
367
6.15
Model Selection
370
6.
1
5.
1 Estimating Confidence Intervals
370
6.15.2
ROC Curves
372
6.
1
6
Summary
373
Exercises
375
Bibliographic Notes
378
Chapter
7
Cluster Analysis
383
7.
1 What Is Cluster Analysis?
383
7.2
Types of Data in Cluster Analysis
386
7.2.1
Interval-Scaled Variables
387
7.2.2
Binary Variables
389
7.2.3
Categorical, Ordinal, and Ratio-Scaled Variables
392
7.2.4
Variables of Mixed Types
395
7.2.5
Vector Objects
397
7.3
A Categorization of Major Clustering Methods
398
7.4
Partitioning Methods
401
7.4.
1 Classical Partitioning Methods: /t-Means and ¿-Medoids
402
7.4.2
Partitioning Methods in Large Databases: From
/t-Medoids to CLARANS
407
7.5
Hierarchical Methods
408
7.5.
1 Agglomerative and Divisive Hierarchical Clustering
408
7.5.2
BIRCH: Balanced Iterative Reducing and Clustering
Using Hierarchies
4
1
2
7.5.3
ROCK: A Hierarchical Clustering Algorithm for
Categorical Attributes
414
7.5.4
Chameleon: A Hierarchical Clustering Algorithm
Using Dynamic Modeling
4
1
6
7 6
Density-Based Methods
4
1
8
7.6.
1 DBSCAN: A Density-Based Clustering Method Based on
Connected Regions with Sufficiently High Density
4
1
8
Contents xv
7.6.2
OPTICS: Ordering Points to Identify the Clustering
Structure
420
7.6.3
DENCLUE: Clustering Based on Density
Distribution Functions
422
7.7
Grid-Based Methods
424
7.7.
1 STING: STatistical INformation Grid
425
7.7.2
WaveCluster: Clustering Using Wavelet Transformation
427
7.8
Model-Based Clustering Methods
429
7.8.
1 Expectation-Maximization
429
7.8.2
Conceptual Clustering
43
1
7.8.3
Neural Network Approach
433
7.9
Clustering High-Dimensional Data
434
7.9.
1 CLIQUE: A Dimension-Growth Subspace Clustering Method
436
7.9.2
PROCLUS: A Dimension-Reduction Subspace Clustering
Method
439
7.9.3
Frequent Pattern-Based Clustering Methods
440
7.
1
0
Constraint-Based Cluster Analysis
444
7.
1
0.
1 Clustering with Obstacle Objects
446
7.10.2
User-Constrained Cluster Analysis
448
7.
1
0.3
Semi-Supervised Cluster Analysis
449
7
I I Outlier Analysis
45
1
7.
1 I
.
I Statistical Distribution-Based Outlier Detection
452
7.
1 1
.2
Distance-Based Outlier Detection
454
7.
1 1
.3
Density-Based Local Outlier Detection
455
7.
1 1
.4
Deviation-Based Outlier Detection
458
7.
1
2
Summary
460
Exercises
46
1
Bibliographic Notes
464
Chapter
8
Mining Stream, Time-Series, and Sequence Data
467
8.
1 Mining Data Streams
468
8.
1
.
1 Methodologies for Stream Data Processing and
Stream Data Systems
469
8.
1
.2
Stream
OLAP
and Stream Data Cubes
474
8.
1
.3
Frequent-Pattern Mining in Data Streams
479
8.
1
.4
Classification of Dynamic Data Streams
48
1
8.
1
.5
Clustering Evolving Data Streams
486
8.2
Mining Time-Series Data
489
8.2.
1 Trend Analysis
490
8.2.2
Similarity Search in Time-Series Analysis
493
xvi Contents
8.3 Mining
Sequence Patterns in Transactional Databases
498
8.3.
1 Sequential Pattern
Mining:
Concepts and
Primitives 498
8.3.2
Scalable Methods for
Mining
Sequential Patterns
500
8.3.3
Constraint-Based Mining of Sequential Patterns
509
8.3.4
Periodicity Analysis for Time-Related Sequence Data
5
1
2
8.4
Mining Sequence Patterns in Biological Data
5
1
3
8.4.
1 Alignment of Biological Sequences
5
1
4
8.4.2
Hidden Markov Model for Biological Sequence Analysis
5
1
8
8.5
Summary
527
Exercises
528
Bibliographic Notes
53
1
Chapter
9
Graph Mining, Social Network Analysis, and
Multirelational
Data Mining
535
9.1
Graph Mining
535
9.
1
.
1 Methods for Mining Frequent Subgraphs
536
9.
1
.2
Mining Variant and Constrained Substructure Patterns
545
9.
1
.3
Applications: Graph Indexing, Similarity Search, Classification,
and Clustering
55
1
9 2
Social Network Analysis
556
9.2.
1 What Is a Social Network?
556
9.2.2
Characteristics of Social Networks
557
9.2.3
Link Mining: Tasks and Challenges
56
1
9.2.4
Mining on Social Networks
565
9 3 Multirelational Data
Mining
57
1
9.3.
1 What Is
Multirelational Data
Mining?
571
9.3.2
ILP Approach to
Multirelational
Classification
573
9.3.3
Tuple ID Propagation
575
9.3.4 Multirelational
Classification Using Tuple ID Propagation
577
9.3.5 Multirelational
Clustering with User Guidance
580
9.4
Summary
584
Exercises
586
Bibliographic Notes
587
Chapter 1
0
Mining Object, Spatial, Multimedia, Text, and Web Data
59
1
1
0.
1 Multidimensional Analysis and Descriptive Mining of Complex
Data Objects
591
1
0.
1
.
1 Generalization of Structured Data
592
10.1.2
Aggregation and Approximation in Spatial and Multimedia Data
Generalization
593
Contents xvii
IO.
1.3
Generalization of Object Identifiers and Class/Subclass
Hierarchies
594
10.1.4
Generalization of Class Composition Hierarchies
595
1
0.
1
.5
Construction and Mining of Object Cubes
596
10.1.6
Generalization-Based Mining of Plan Databases by
Divide-and-Conquer
596
1
0.2
Spatial Data Mining
600
10.2.
1 Spatial Data Cube Construction and Spatial
OLAP 601
1
0.2.2
Mining Spatial Association and Co-location Patterns
605
1
0.2.3
Spatial Clustering Methods
606
1
0.2.4
Spatial Classification and Spatial Trend Analysis
606
10.2.5
Mining Raster Databases
607
1
0.3
Multimedia Data Mining
607
1
0.3.
1 Similarity Search in Multimedia Data
608
1
0.3.2
Multidimensional Analysis of Multimedia Data
609
10.3.3
Classification and Prediction Analysis of Multimedia Data
611
10.3.4
Mining Associations in Multimedia Data
612
1
0.3.5
Audio and Video Data Mining
6
1
3
10.4
Text Mining
614
1
0.4.
1 Text Data Analysis and Information Retrieval
6
1
5
10.4.2
Dimensionality Reduction for Text
621
10.4.3
Text Mining Approaches
624
1
0.5
Mining the World Wide Web
628
1
0.5.
1 Mining the Web Page Layout Structure
630
1
0.5.2
Mining the Web's Link Structures to Identify
Authoritative Web Pages
63
1
1
0.5.3
Mining Multimedia Data on the Web
637
10.5.4
Automatic Classification of Web Documents
638
10.5.5
Web Usage Mining
640
1
0.6
Summary
64
1
Exercises
642
Bibliographic Notes
645
Chapter I I Applications and Trends in Data Mining
649
III Data Mining Applications
649
I I.I.I Data Mining for Financial Data Analysis
649
I 1
.
1
.2
Data Mining for the Retail Industry
65
1
I 1
.
1
.3
Data Mining for the Telecommunication Industry
652
I 1
.
1
.4
Data Mining for Biological Data Analysis
654
I 1
.
1
.5
Data Mining in Other Scientific Applications
657
I 1
.
1
.6
Data Mining for Intrusion Detection
658
xviii Contents
I 1
.2
Data Mining System Products and Research Prototypes
660
I 1
.2.
1 How to Choose a Data Mining System
660
I 1
.2.2
Examples of Commercial Data Mining Systems
663
I 1
.3
Additional Themes on Data Mining
665
I 1
.3.
1 Theoretical Foundations of Data Mining
665
I 1
.3.2
Statistical Data Mining
666
I 1
.3.3
Visual and Audio Data Mining
667
I 1
.3.4
Data Mining and Collaborative Filtering
670
I 1
.4
Social Impacts of Data Mining
675
I 1
.4.
1 Ubiquitous and Invisible Data Mining
675
I 1
.4.2
Data Mining, Privacy, and Data Security
678
I 1
.5
Trends in Data Mining
68
1
I
1.6
Summary
684
Exercises
685
Bibliographic Notes
687
Appendix An Introduction to Microsoft's OLE
DB
for
Data Mining
69
1
A. I Model Creation
693
A.2 Model Training
695
A.3 Model Prediction and Browsing
697
Bibliography
703
Index
745 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Han, Jiawei 1949- Kamber, Micheline |
| author_GND | (DE-588)137798342 |
| author_facet | Han, Jiawei 1949- Kamber, Micheline |
| author_role | aut aut |
| author_sort | Han, Jiawei 1949- |
| author_variant | j h jh m k mk |
| building | Verbundindex |
| bvnumber | BV020863175 |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.9.D343 |
| callnumber-search | QA76.9.D343 |
| callnumber-sort | QA 276.9 D343 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | QH 500 ST 270 ST 530 |
| ctrlnum | (OCoLC)254907652 (DE-599)BVBBV020863175 |
| dewey-full | 005.741 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.741 |
| dewey-search | 005.741 |
| dewey-sort | 15.741 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV020863175 |
| illustrated | Not Illustrated |
| index_date | 2024-07-02T13:23:39Z |
| indexdate | 2024-07-09T20:26:56Z |
| institution | BVB |
| isbn | 1558609016 9781558609013 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014185035 |
| oclc_num | 254907652 |
| open_access_boolean | 1 |
| owner | DE-355 DE-BY-UBR DE-384 DE-473 DE-BY-UBG DE-M347 DE-20 DE-29T DE-739 DE-1028 DE-945 DE-N2 DE-703 DE-1051 DE-523 DE-83 DE-706 DE-525 DE-188 |
| owner_facet | DE-355 DE-BY-UBR DE-384 DE-473 DE-BY-UBG DE-M347 DE-20 DE-29T DE-739 DE-1028 DE-945 DE-N2 DE-703 DE-1051 DE-523 DE-83 DE-706 DE-525 DE-188 |
| physical | XXVIII, 770 S. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Elsevier |
| record_format | marc |
| series2 | The Morgan Kaufmann series in data management systems |
| spelling | Han, Jiawei 1949- Verfasser (DE-588)137798342 aut Data mining concepts and techniques Jiawei Han ; Micheline Kamber 2. ed. Amsterdam [u.a.] Elsevier 2006 XXVIII, 770 S. txt rdacontent n rdamedia nc rdacarrier The Morgan Kaufmann series in data management systems Literaturverz. S. 703-743 Exploration de données (Informatique) Veri madenciliği Data mining Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s DE-604 Kamber, Micheline Verfasser aut http://www.loc.gov/catdir/enhancements/fy0664/2006296324-d.html Publisher description kostenfrei DE-601 pdf/application http://www.gbv.de/dms/bowker/toc/9781558609013.pdf Inhaltsverzeichnis Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014185035&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Han, Jiawei 1949- Kamber, Micheline Data mining concepts and techniques Exploration de données (Informatique) Veri madenciliği Data mining Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4428654-5 |
| title | Data mining concepts and techniques |
| title_auth | Data mining concepts and techniques |
| title_exact_search | Data mining concepts and techniques |
| title_exact_search_txtP | Data mining concepts and techniques |
| title_full | Data mining concepts and techniques Jiawei Han ; Micheline Kamber |
| title_fullStr | Data mining concepts and techniques Jiawei Han ; Micheline Kamber |
| title_full_unstemmed | Data mining concepts and techniques Jiawei Han ; Micheline Kamber |
| title_short | Data mining |
| title_sort | data mining concepts and techniques |
| title_sub | concepts and techniques |
| topic | Exploration de données (Informatique) Veri madenciliği Data mining Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Exploration de données (Informatique) Veri madenciliği Data mining Data Mining |
| url | http://www.loc.gov/catdir/enhancements/fy0664/2006296324-d.html http://www.gbv.de/dms/bowker/toc/9781558609013.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014185035&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hanjiawei dataminingconceptsandtechniques AT kambermicheline dataminingconceptsandtechniques |