Data mining:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Oxford [u.a.]
Oxford Univ. Press
2009
|
| Edition: | 1. publ. |
| Series: | Oxford higher education
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | X, 342 S. Ill., graph. Darst. |
| ISBN: | 0195686284 9780195686289 |
Staff View
MARC
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| 245 | 1 | 0 | |a Data mining |c Vikram Pudi ; P. Radha Krishna |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Oxford [u.a.] |b Oxford Univ. Press |c 2009 | |
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Record in the Search Index
| _version_ | 1804137356784041984 |
|---|---|
| adam_text | Contents
Preface
Acknowledgements
Chapter
1
Introduction
1.1
Motivation
7
1.2
Data Warehousing and Data Mining Technologies
5
1.3
Data Models
6
1.4
Data Warehousing and
OLAP:
User s Perspective
7
1.5
Data Mining: User s Perspective
77
1.6
Related Disciplines
14
1.7
Other Issues
77
1.8
Future Trends
20
Summary
20
Exercises
21
Chapter
2
Frequent Pattern Mining
Introduction
22
2.1
Basic Problem Definition
23
2.2
Mining Association Rules
25
2.3
Applications
27
m
v
22
viii Contents
2.4
Variations
35
2.5
Interestingness
39
2.6
Frequent
Itemset
Mining
(FIM)
Algorithms
43
2.7
Current Status of
FIM
Algorithm Comparison
66
2.8
Optimal
FIM
Algorithms
67
2.9
Incremental Mining
77
2.10
Conciseness of Results
78
2.11
Sequential Rules
80
Summary
81
Exercises
83
Chapter
3
Classification
86
Introduction
86
3.1
Basic Problem Definition
87
3.2
Applications
89
3.3
Evaluation of Classifiers
90
3.4
Other Issues
94
3.5
Classification Techniques
700
3.6
Optimal Classification Algorithms
777
3.7
Regression
121
Summary
723
Exercises
723
125
Chapter
4
Clusturing
Introduction
125
4.1
Basic Problem Definition
126
4.2
Clustering: Applications
728
4.3
Measurement of Similarity
730
4.4
Evaluation of Clustering Algorithms
735
4.5
Classification of Clustering Algorithms
738
4.6
Partitioning Methods
739
4.7
Hierarchical Methods
727
4.8
Density-based Methods
146
Contents ix
4.9
Grid-based Methods
158
4.10
Outlier Detection
159
Summary
162
Exercises
163
Chapter
5
Pattern Discovery in Real-World Data
Introduction
166
5.1
Relational Data
166
5.2
Transactional Data
770
5.3
Multi-Dimensional Data
176
5.4
Distributed Data
775
5.5
Spatial Data
181
5.6
Data Streams
183
5.7
Time-Series Data
191
5.8
Text and Web Data
194
5.9
Multimedia Data
203
Summary
205
Exercises
205
Chapter
6
Data Warehousing: The Data Model
Introduction
207
6.1
Fundamentals
207
6.2
Data Warehouse Data Characteristics
216
6.3
Data Warehouse Components
275
6.4
Approaches to Build Data Marts and Data Warehouse
223
6.5
ETL
224
6.6
Logical Data Modeling
235
6.7 Schemas
Design in Dimensional Modeling
244
6.8 OLAP 249
6.9
Storage and Chunks
258
Summary
266
Exercises
267
165
207
χ
Contents
Chapter
7
Data Warehousing: Query Processing
Introduction
269
7.1
Materialized Views
271
7.2
Materialized Views Selection
275
7.3
Materialized View Maintenance and Consistency
281
7.4
Indexűig
283
1.5
General Query Evaluation
294
Summary
299
Exercises
300
269
Chapter
8
Case Studies
Introduction
302
8.1
Study
1 :
Telecom Content Warehouse
3 03
8.2
Study
2: OLAP
for the Fast Food Industry
309
8.3
Study
3:
Prototype Credit DataMart for a Bank
310
8.4
Study
4:
Churn Modeling for a Bank
317
8.5
Study
5:
Intrusion Detection using kNN classification
325
Summary
329
Exercises
329
302
Chapter
9
Current Trends in Pattern Discovery
Introduction
330
9.1
Ten Challenging Problems
331
Summary
335
330
Index
337
|
| adam_txt |
Contents
Preface
Acknowledgements
Chapter
1
Introduction
1.1
Motivation
7
1.2
Data Warehousing and Data Mining Technologies
5
1.3
Data Models
6
1.4
Data Warehousing and
OLAP:
User's Perspective
7
1.5
Data Mining: User's Perspective
77
1.6
Related Disciplines
14
1.7
Other Issues
77
1.8
Future Trends
20
Summary
20
Exercises
21
Chapter
2
Frequent Pattern Mining
Introduction
22
2.1
Basic Problem Definition
23
2.2
Mining Association Rules
25
2.3
Applications
27
m
v
22
viii Contents
2.4
Variations
35
2.5
Interestingness
39
2.6
Frequent
Itemset
Mining
(FIM)
Algorithms
43
2.7
Current Status of
FIM
Algorithm Comparison
66
2.8
Optimal
FIM
Algorithms
67
2.9
Incremental Mining
77
2.10
Conciseness of Results
78
2.11
Sequential Rules
80
Summary
81
Exercises
83
Chapter
3
Classification
86
Introduction
86
3.1
Basic Problem Definition
87
3.2
Applications
89
3.3
Evaluation of Classifiers
90
3.4
Other Issues
94
3.5
Classification Techniques
700
3.6
Optimal Classification Algorithms
777
3.7
Regression
121
Summary
723
Exercises
723
125
Chapter
4
Clusturing
Introduction
125
4.1
Basic Problem Definition
126
4.2
Clustering: Applications
728
4.3
Measurement of Similarity
730
4.4
Evaluation of Clustering Algorithms
735
4.5
Classification of Clustering Algorithms
738
4.6
Partitioning Methods
739
4.7
Hierarchical Methods
727
4.8
Density-based Methods
146
Contents ix
4.9
Grid-based Methods
158
4.10
Outlier Detection
159
Summary
162
Exercises
163
Chapter
5
Pattern Discovery in Real-World Data
Introduction
166
5.1
Relational Data
166
5.2
Transactional Data
770
5.3
Multi-Dimensional Data
176
5.4
Distributed Data
775
5.5
Spatial Data
181
5.6
Data Streams
183
5.7
Time-Series Data
191
5.8
Text and Web Data
194
5.9
Multimedia Data
203
Summary
205
Exercises
205
Chapter
6
Data Warehousing: The Data Model
Introduction
207
6.1
Fundamentals
207
6.2
Data Warehouse Data Characteristics
216
6.3
Data Warehouse Components
275
6.4
Approaches to Build Data Marts and Data Warehouse
223
6.5
ETL
224
6.6
Logical Data Modeling
235
6.7 Schemas
Design in Dimensional Modeling
244
6.8 OLAP 249
6.9
Storage and Chunks
258
Summary
266
Exercises
267
165
207
χ
Contents
Chapter
7
Data Warehousing: Query Processing
Introduction
269
7.1
Materialized Views
271
7.2
Materialized Views Selection
275
7.3
Materialized View Maintenance and Consistency
281
7.4
Indexűig
283
1.5
General Query Evaluation
294
Summary
299
Exercises
300
269
Chapter
8
Case Studies
Introduction
302
8.1
Study
1 :
Telecom Content Warehouse
3 03
8.2
Study
2: OLAP
for the Fast Food Industry
309
8.3
Study
3:
Prototype Credit DataMart for a Bank
310
8.4
Study
4:
Churn Modeling for a Bank
317
8.5
Study
5:
Intrusion Detection using kNN classification
325
Summary
329
Exercises
329
302
Chapter
9
Current Trends in Pattern Discovery
Introduction
330
9.1
Ten Challenging Problems
331
Summary
335
330
Index
337 |
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| index_date | 2024-07-02T19:44:07Z |
| indexdate | 2024-07-09T21:11:00Z |
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| isbn | 0195686284 9780195686289 |
| language | English |
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| spelling | Pudi, Vikram Verfasser aut Data mining Vikram Pudi ; P. Radha Krishna 1. publ. Oxford [u.a.] Oxford Univ. Press 2009 X, 342 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Oxford higher education Data mining Data Mining (DE-588)4428654-5 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Data Mining (DE-588)4428654-5 s DE-604 Radha Krishna, P. Verfasser aut Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016302331&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Pudi, Vikram Radha Krishna, P. Data mining Data mining Data Mining (DE-588)4428654-5 gnd |
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| title | Data mining |
| title_auth | Data mining |
| title_exact_search | Data mining |
| title_exact_search_txtP | Data mining |
| title_full | Data mining Vikram Pudi ; P. Radha Krishna |
| title_fullStr | Data mining Vikram Pudi ; P. Radha Krishna |
| title_full_unstemmed | Data mining Vikram Pudi ; P. Radha Krishna |
| title_short | Data mining |
| title_sort | data mining |
| topic | Data mining Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data mining Data Mining Lehrbuch |
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