Soft computing for data mining applications:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2009
|
| Schriftenreihe: | Studies in computational intelligence
190 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben |
| Beschreibung: | XXII, 341 S. Ill., graph. Darst. |
| ISBN: | 9783642001925 9783642001932 |
Internformat
MARC
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| 020 | |a 9783642001932 |9 978-3-642-00193-2 | ||
| 035 | |a (OCoLC)319157544 | ||
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| 100 | 1 | |a Venugopal, K. R. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Soft computing for data mining applications |c K. R. Venugopal ; K. G. Srinivasa ; L. M. Patnaik |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2009 | |
| 300 | |a XXII, 341 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Studies in computational intelligence |v 190 | |
| 500 | |a Literaturangaben | ||
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| 689 | 1 | 1 | |a Genetischer Algorithmus |0 (DE-588)4265092-6 |D s |
| 689 | 1 | |5 DE-604 | |
| 700 | 1 | |a Srinivasa, K. G. |e Verfasser |4 aut | |
| 700 | 1 | |a Patnaik, L. M. |e Verfasser |4 aut | |
| 830 | 0 | |a Studies in computational intelligence |v 190 |w (DE-604)BV020822171 |9 190 | |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-020408059 | ||
Datensatz im Suchindex
| _version_ | 1804143044572741632 |
|---|---|
| adam_text | Contents
1
Introduction
............................................. 1
1.1 Data Mining.......................................... 4
1.1.1
Association
Rule Mining (ARM)
.................. 4
1.1.2
Incremental Mining
.............................. 5
1.1.3
Distributed Data Mining
......................... 6
1.1.4
Sequential Mining
............................... 6
1.1.5
Clustering
...................................... 6
1.1.6
Classification
................................... 8
1.1.7
Characterization
................................ 8
1.1.8
Discrimination
.................................. 9
1.1.9
Deviation Mining
................................ 9
1.1.10
Evolution Mining
................................ 9
1.1.11
Prediction
...................................... 10
1.1.12
Web Mining
.................................... 10
1.1.13
Text Mining
.................................... 11
1.1.14
Data Warehouses
................................ 11
1.2
Soft Computing
....................................... 13
1.2.1
Importance of Soft Computing
.................... 13
1.2.2
Genetic Algorithms
.............................. 13
1.2.3
Neural Networks
................................ 14
1.2.4
Support Vector Machines
......................... 14
1.2.5
Fuzzy Logic
.................................... 15
1.2.6
Rough Sets
..................................... 16
1.3
Data Mining Applications
.............................. 16
References
................................................ 17
2
Self Adaptive Genetic Algorithms
....................... 19
2.1
Introduction
.......................................... 19
2.2
Related Work
......................................... 20
2.3
Overview
............................................. 22
2.4
Algorithm
............................................ 23
2.4.1 Problem Definition.............................. 23
2.4.2
Pseudocode.....................................
23
2.5
Mathematical Analysis
................................. 25
2.5.1
Convergence Analysis
............................ 30
2.6
Experiments
.......................................... 32
2.7
Performance Analysis
.................................. 40
2.8
A Heuristic Template Based Adaptive Genetic
Algorithms
........................................... 42
2.8.1
Problem Definition
.............................. 42
2.9
Example
............................................. 42
2.10
Performance Analysis of HTAGA
........................ 44
2.11
Summary
............................................. 48
References
................................................ 49
Characteristic Amplification Based Genetic
Algorithms
.............................................. 51
3.1
Introduction
.......................................... 51
3.2
Formalizations
........................................ 52
3.3
Design Issues
......................................... 54
3.4
Algorithm
............................................ 55
3.5
Results and Performance Analysis
....................... 58
3.6
Summary
............................................. 61
References
................................................ 61
Dynamic Association Rule Mining Using Genetic
Algorithms
.............................................. 63
4.1
Introduction
.......................................... 63
4.1.1
Inter Transaction Association Rule Mining
......... 64
4.1.2
Genetic Algorithms
.............................. 65
4.2
Related Work
......................................... 66
4.3
Algorithms
........................................... 67
4.4
Example
............................................. 69
4.5
Performance Analysis
.................................. 74
4.5.1
Experiments on Real Data
........................ 78
4.6
Summary
............................................. 79
References
................................................ 79
Evolutionary Approach for XML Data Mining
........... 81
5.1
Semantic Search over XML Corpus
...................... 82
5.2
The Existing Problem
.................................. 83
5.2.1
Motivation
..................................... 84
5.3
XML Data Model and Query Semantics
.................. 85
5.4
Genetic Learning of Tags
............................... 86
5.5
Search Algorithm
...................................... 89
5.5.1
Identification Scheme
............................ 89
5.5.2
Relationship Strength
............................ 90
5.5.3
Semantic Interconnection
......................... 91
5.6
Performance Studies
................................... 93
5.7
Selective Dissemination of XML Documents
.............. 99
5.8
Genetic Learning of User Interests
....................... 101
5.9
User Model Construction
............................... 102
5.9.1
SVM for User Model Construction
................. 103
5.10
Selective Dissemination
................................ 103
5.11
Performance Analysis
.................................. 105
5.12
Categorization Using SVMs
............................. 108
5.12.1
XML Topic Categorization
....................... 108
5.12.2
Feature Set Construction
......................... 109
5.13
SVM for Topic Categorization
...........................
Ill
5.14
Experimental Studies
.................................. 113
5.15
Summary
............................................. 116
References
................................................ 117
Soft Computing Based CBIR System
.................... 119
6.1
Introduction
.......................................... 119
6.2
Related Work
......................................... 120
6.3
Model
................................................ 121
6.3.1
Pre-processing
.................................. 122
6.3.2
Feature Extraction
.............................. 122
6.3.3
Feature Clustering
............................... 126
6.3.4
Classification
................................... 126
6.4
The STIRF System
.................................... 128
6.5
Performance Analysis
.................................. 129
6.6
Summary
............................................. 136
References
................................................ 136
Fuzzy Based Neuro
-
Genetic Algorithm for Stock
Market Prediction
....................................... 139
7.1
Introduction
.......................................... 139
7.2
Related Work
......................................... 140
7.3
Model
................................................ 141
7.4
Algorithm
............................................ 146
7.4.1
Algorithm FEASOM
............................. 146
7.4.2
Modified Kohonen Algorithm
..................... 146
7.4.3
The Genetic Algorithm
.......................... 148
7.4.4
Fuzzy Inference System
.......................... 149
7.4.5
Backpropagation Algorithm
....................... 149
7.4.6
Complexity
..................................... 149
7.5
Example
............................................. 150
7.6
Implementation
....................................... 152
7.7
Performance Analysis
.................................. 154
7.8
Summary
............................................. 165
References
................................................ 165
8
Data Mining Based Query Processing Using Rough
Sets and GAs
............................................ 167
8.1
Introduction
.......................................... 167
8.2
Problem Definition
.................................... 169
8.3
Architecture
.......................................... 170
8.3.1
Rough Sets
..................................... 171
8.3.2
Information Streaks
............................. 174
8.4
Modeling of Continuous-Type Data
...................... 175
8.5
Genetic Algorithms and Query Languages
................ 180
8.5.1
Associations
.................................... 181
8.5.2
Concept Hierarchies
............................. 182
8.5.3
Dealing with Rapidly Changing Data
.............. 185
8.6
Experimental Results
.................................. 186
8.7
Adaptive Data
Aíining
Using Hybrid Model of Rough Sets
and Two-Phase GAs
................................... 189
8.8
Mathematical Model of Attributes (MMA)
............... 190
8.9
Two Phase Genetic Algorithms
.......................... 191
8.10
Summary
............................................. 194
References
................................................ 194
9
Hashing the Web for Better Reorganization
............. 197
9.1
Introduction
.......................................... 197
9.1.1
Frequent Items and Association Rules
.............. 198
9.2
Related Work
......................................... 200
9.3
Web Usage Mining and Web Reorganization Model
........ 200
9.4
Problem Definition
.................................... 202
9.5
Algorithms
........................................... 202
9.5.1
Classification of Pages
........................... 206
9.6
Pre-processing
........................................ 206
9.7
Example
............................................. 208
9.8
Performance Analysis
.................................. 210
9.9
Summary
............................................. 214
References
................................................ 214
10
Algorithms for Web Personalization
..................... 217
10.1
Introduction
.......................................... 217
10.2
Overview
............................................. 219
10.3
Data Structures
....................................... 219
10.4
Algorithm
............................................ 221
10.5
Performance Analysis
.................................. 223
10.6
Summary
............................................. 229
References
................................................ 229
11
Classifying Clustered
Webpages
for Effective
Personalization
.......................................... 231
11.1
Introduction
.......................................... 231
11.2
Related Work
......................................... 232
11.3
Proposed System
...................................... 233
11.4
Example
............................................. 237
11.5
Algorithm II: Naive Bayesian Probabilistic Model
.......... 239
11.6
Performance Analysis
.................................. 241
11.7
Summary
............................................. 246
References
................................................ 247
12
Mining Top
-
к
Ranked Webpages Using
SA
and GA
.... 249
12.1
Introduction
.......................................... 249
12.2
Algorithm TkRSAGA
.................................. 252
12.3
Performance Analysis
.................................. 253
12.4
Summary
............................................. 258
References
................................................ 258
13
A Semantic Approach for Mining Biological
Databases
............................................... 259
13.1
Introduction
......................................... 259
13.2
Understanding the Nature of Biological Data
............. 260
13.3
Related Work
........................................ 262
13.4
Problem Definition
.................................... 263
13.5
Identifying Indexing Technique
......................... 263
13.6
LSI Model
........................................... 265
13.7
Search Optimization Using GAs
........................ 266
13.8
Proposed Algorithm
................................... 267
13.9
Performance Analysis
................................. 268
13.10
Summary
............................................ 277
References
................................................ 277
14
Probabilistic Approach for
DNA
Compression
........... 279
14.1
Introduction
.......................................... 279
14.2
Probability Model
..................................... 281
14.3
Algorithm
............................................ 284
14.4
Optimization of P
.................................... 285
14.5
An Example
.......................................... 286
14.6
Performance Analysis
.................................. 287
14.7
Summary
............................................. 288
References
................................................ 288
15
Non-repetitive
DNA
Compression Using Memoization
... 291
15.1
Introduction
.......................................... 291
15.2
Related Work
......................................... 293
15.3
Algorithm
............................................ 294
15.4
Experimental Results
.................................. 298
15.5
Summary
............................................. 300
References
................................................ 300
16
Exploring Structurally Similar Protein Sequence
Motifs
................................................... 303
16.1
Introduction
.......................................... 303
16.2
Related Work
......................................... 305
16.3
Motifs in Protein Sequences
............................. 305
16.4
Algorithm
............................................ 307
16.5
Experimental Setup
.................................... 308
16.6
Experimental Results
.................................. 310
16.7
Summary
............................................. 317
References
................................................ 317
17
Matching Techniques in Genomic Sequences for Motif
Searching
................................................ 319
17.1
Overview
............................................. 319
17.2
Related Work
......................................... 320
17.3
Introduction
.......................................... 321
17.4
Alternative Storage and Retrieval Technique
.............. 323
17.5
Experimental Setup and Results
......................... 327
17.6
Summary
............................................. 329
References
................................................ 330
18
Merge Based Genetic Algorithm for Motif Discovery
.... 331
18.1
Introduction
.......................................... 331
18.2
Related Work
......................................... 334
18.3
Algorithm
............................................ 334
18.4
Experimental Setup
.................................... 337
18.5
Performance Analysis
.................................. 339
18.6
Summary
............................................. 340
References
................................................ 340
|
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| genre_facet | Aufsatzsammlung |
| id | DE-604.BV036485353 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:41:24Z |
| institution | BVB |
| isbn | 9783642001925 9783642001932 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020408059 |
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| open_access_boolean | |
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| owner_facet | DE-11 DE-739 |
| physical | XXII, 341 S. Ill., graph. Darst. |
| publishDate | 2009 |
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| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spelling | Venugopal, K. R. Verfasser aut Soft computing for data mining applications K. R. Venugopal ; K. G. Srinivasa ; L. M. Patnaik Berlin [u.a.] Springer 2009 XXII, 341 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 190 Literaturangaben Data Mining (DE-588)4428654-5 gnd rswk-swf Soft Computing (DE-588)4455833-8 gnd rswk-swf Genetischer Algorithmus (DE-588)4265092-6 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Data Mining (DE-588)4428654-5 s Soft Computing (DE-588)4455833-8 s DE-604 Genetischer Algorithmus (DE-588)4265092-6 s Srinivasa, K. G. Verfasser aut Patnaik, L. M. Verfasser aut Studies in computational intelligence 190 (DE-604)BV020822171 190 Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020408059&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Venugopal, K. R. Srinivasa, K. G. Patnaik, L. M. Soft computing for data mining applications Studies in computational intelligence Data Mining (DE-588)4428654-5 gnd Soft Computing (DE-588)4455833-8 gnd Genetischer Algorithmus (DE-588)4265092-6 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4455833-8 (DE-588)4265092-6 (DE-588)4143413-4 |
| title | Soft computing for data mining applications |
| title_auth | Soft computing for data mining applications |
| title_exact_search | Soft computing for data mining applications |
| title_full | Soft computing for data mining applications K. R. Venugopal ; K. G. Srinivasa ; L. M. Patnaik |
| title_fullStr | Soft computing for data mining applications K. R. Venugopal ; K. G. Srinivasa ; L. M. Patnaik |
| title_full_unstemmed | Soft computing for data mining applications K. R. Venugopal ; K. G. Srinivasa ; L. M. Patnaik |
| title_short | Soft computing for data mining applications |
| title_sort | soft computing for data mining applications |
| topic | Data Mining (DE-588)4428654-5 gnd Soft Computing (DE-588)4455833-8 gnd Genetischer Algorithmus (DE-588)4265092-6 gnd |
| topic_facet | Data Mining Soft Computing Genetischer Algorithmus Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020408059&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT venugopalkr softcomputingfordataminingapplications AT srinivasakg softcomputingfordataminingapplications AT patnaiklm softcomputingfordataminingapplications |