Data mining II:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Southampton [u.a.]
WIT Press
2000
|
| Schriftenreihe: | Management information systems
2 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | 628 S. graph. Darst. |
| ISBN: | 185312821X |
Internformat
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| 245 | 1 | 0 | |a Data mining II |c ed.: N. Ebecken ... |
| 264 | 1 | |a Southampton [u.a.] |b WIT Press |c 2000 | |
| 300 | |a 628 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Management information systems |v 2 | |
| 650 | 4 | |a Data Mining - Congrès | |
| 650 | 7 | |a Data mining |2 gtt | |
| 650 | 7 | |a Data warehouse |2 gtt | |
| 650 | 4 | |a Data mining |v Congresses | |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-009135828 | ||
Datensatz im Suchindex
| _version_ | 1804128174040154112 |
|---|---|
| adam_text | Contents
Section
1: Applications
of Data
Mining in Science,
Engineering, Business,
Industry and Medicine
Web
mining through the
online
analyst
A. Zanosi
...............................................................................................................
З
Bayesian networks for knowledge discovery in a database from
the program for genetic improvement of the Nelore breed
S.O.
Rezende,
CAJ.
da Rocha
&
R.B.
Lobo
...................................................... 15
Using a data mining workbench for micro and macro
economic modelling
D.F. Nettleton,
V.L.
Fondino,
M.
Witty
&
E. Vilajosana
....................................25
Small business modeling within the financial accounting
conceptual framework
A.B.
Thornton— Trump
&
W.
Fu
........................................................................35
Paperwork reduction by means of data mining
A. Pagnoni, S.
Parisi
&
D.
Attorrese
..................................................................45
CRM
in a real-world insurance company
G. Pedrazzi, R.
Turra
&
A. Zanasi
.....................................................................53
A data-mining alternative to model hospital operations:
clinical costs and predictions
D.
Riaño
&
S.
Prado
...........................................................................................63
Optimal entrocopy encoders for mining multiply resolved data
R.A. DeVore, LS. Johnson,
С
Pan
&
R.C. Sharpley
.........................................73
Acquisition of KANSEI based on fuzzy inference and
m u
Iti vantate
analysis
T. Fukuda,
T. Chikazawa, Y. Hasegawa, F. Kobayashi,
K. Shimojima
&
Y. Yamaguchi
...........................................................................83
Data
mining for database
marketing
at
Garanti Bank
O.F.
Alis,
E.
Karakurt
&
P. Melli
.......................................................................93
Use of equation discovery: Oscillating flow in a U-tube
G.
Petkovšek&B. Kompare
............................................................................. 109
The use of learning classifier systems in the direct marketing industry
A. Greenyer
....................................................................................................... 119
Section
2:
Data Warehousing and Databases
KR and model discovery from active
DB
with predictive logic
CF.
Nourani
&
G.S.L Loo
.............................................................................. 131
Metadata
-
based data auditing
H. Hinrichs&T. Wiïkens
................................................................................. 141
An analysis of the integration between data mining applications
and database systems
E.
Bezerra,
M.
Mattoso &
G. Xexéo
................................................................. 151
Design
of a data warehouse to
support
the
management
of
academic institutions
L.
Marín,
M. Hassan, A.
Lozano,
G. Manassero
&
O.
Chiotti
......................... 161
Section
3:
Internet Applications
Automatic construction
of ontology from text databases
N.
Zhong, Y.Y. Yao& Y. Kakemoto
.................................................................. 173
Attractability: an indicator for optimising the design of a web site
A. Platis, P. Tselios& G. Vouros
..................................................................... 181
Section
4:
Data Mining Methodologies
A new algorithm for finding association rules
L. Dumitriu, C. Tudorie, E. Pecheanu
&
A.
Istrate.......................................... 195
Local feature selection for heterogeneous problems
/.
Skrypnyk, A. TsymbalSc S. Puuronen
............................................................203
Content in context: a data-driven approach
J. Vernau
.......................................................................... 213
Section
5:
Knowledge
Discovery and Data
Mining
An architecture
for ABEN-KDD- an agent-based environment for
knowledge discovery in databases
M.
Sousa,
M. Gottgtroy,
N. Ebecken&
Μ.
Rodrigues
......................................221
Discovering graph structures in high dimensional spaces
V.
Dubois
&
M
Quafafou
.................................................................................231
Discovering salient data features by composing and manipulating
logical equations
D.E.
Sitnikov, B. D Cruz&P.E. Sitnikova
.......................................................241
A qualitative spatial reasoning approach in knowledge discovery
in spatial databases
M
Santos
&
L.
Amaral
.....................................................................................249
A fuzzy
-
based conceptual KDD approach: the SaintEtiQ system
G.
Raschia
&N. Mouaddib
..............................................................................259
Supervised knowledge discovery from incomplete data
A. Kalousis
&
M.
Hilario
..................................................................................269
Data scale reduction via instances summarization using
the Rough Set Theory
G. Gaunter
&
M. Quafafou
...............................................................................279
Data mining in temporal sequences: a technique based on
MC
S.
Massa
&
P.P. Puliafito
.................................................................................289
Data mining telecommunications network data for fault management
and development testing
R.
Sterriti,
K. Adamson,
CM.
Shapcott
&
Е.Р.
Curran
....................................299
Higher order mining: modelling and mining the results of
knowledge discovery
M. Spiliopoulou
&
J.F. Roddick
.......................................................................309
A computational environment for extracting rules from databases
J.A. Baranauskas, M.C Monard
&
G.E.A.P.A. Batista
....................................321
Considerations about the effectiveness of inductive learning process
in data-mining context
F. Souza&M. Gottgtroy
..................................................................................331
Section
6:
Neural Networks and Decision Trees
The deterministic evolutionary learning algorithm
R. Tsaih
.............................................................................................................343
Wrapped feature selection for binary classification Bayesian
régularisation
neural networks: a database marketing application
5.
Viaene, B. Baesens, D. Van
den Poel,
G. Dedené,
J. Vandenbulcke
&
J. Vanthienen
.....................................................................353
Web
text mining using
a hybrid
intelligent system based on KDT,
expert system and neural network
F.H. Fukuda, E.L.P.
Passos,
M.A.
Pacheco,
LB.
Neto, J.
Valerio,
V.
De Roberto
Junior,
E.R. Antonio
&
L. Chiganer
.........................................363
Alleviating the complexity of the Combinatorial Neural Model using a
committee machine
H. A. do
Prado,
K.F.
Machado
&
P.M.
Engel..................................................373
Application of decision tree classifiers to computer intrusion detection
N.
Ye
&
X.Li
.....................................................................................................381
Effects of attribute selection measures and sampling policies on
functional structures of decision trees
H. Du, S.
Jassim
&
M.F.
Obatusin...................................................................391
Section
7:
Genetic Algorithms and Parallel Techniques
Credit approval by a clustering genetic algorithm
E.R. Hruschka
&
N.F.F. Ebecken
.....................................................................403
Designing optimized pattern recognition systems by learning
Voronoi vectors using genetic algorithms
C.M.N.A.
Pereira
&
R. Schirru
........................................................................413
Scalable parallel algorithms for predictive modelling
P. Christen, M. Hegland, O. Nielsen, S. Roberts
&
I. Altas
.............................423
Section
8:
Visualisation in Data Mining
Interactive
rale-network layout with a genetic algorithm in a
knowledge discovery process
P. Kuntz, R.
Lehn &
H. Briand
.........................................................................435
Visualisation for Data Mining telecommunications network data
R.
Sterriti,
E.P.
Curran,
K. Adamson
&
CM.
Shapcott.
...................................445
InfoZoom
-
Analysing Formula One racing results with an interactive
data mining and visualisation tool
M. Spenke&C. Beilken
....................................................................................455
Section
9:
Clustering and Classification Techniques
Evolving TSK fuzzy rules for classification tasks by Genetic Algorithms
R.P.
Espíndola
&
N.F.F. Ebecken
....................................................................467
Hierachical clustering for data mining by RBF network
Ö.
Ciftcioglu
&
S. Sariyildiz
.............................................................................477
Detecting visual feature importance via tree classifiers. An experience
С
Brambilla,
I.
Gagliardi,
R.
Schettini
&
A. Valsasna
....................................487
Input
dependent misclassification costs for cost-sensitive classifiers
J. Hollmén,
M. Skubacz&M. Taniguchi
..........................................................495
Undirect
knowledge discovery by using singular value decomposition
E.
Maltseva,
С.
Pizzuti
&
D.
Talia
...................................................................505
A clustering algorithm using the tabu search approach with
simulated annealing
S
-С.
Chu
&
J.F.
Roddick
..................................................................................515
Cluster generation using tabu search based maximum descent algorithm
J.S. Pan, S.C.
Chu
&
Z.M.
Lu
...........................................................................525
Stabilization of regression trees
T. Urbana T. Kämpke
.....................................................................................
535
Influence
of lossy compression on hyperspectral image
classification accuracy
J.
Mìnguillón,
J.
Pujol,
J.
Serra
&
I. Ortuño
....................................................545
Section
10:
Tools for Pattern Discovery
Modeling dynamical systems by recurrent neural networks
H.G.
Zimmermann &
R.
Neuneier....................................................................557
A visual data mining tool to support cooperative learning
J.G.
le Roux
&
H.L. Viktor
...............................................................................567
A new insight into the algebraic structure of the exponential smoothing
algorithm of Brown
A. Bellacicco
.....................................................................................................577
Section
11:
Case Studies
A case based reasoning framework to extract knowledge from data
F.
Rodrigues,
С.
Ramosa
P. Henriques
..........................................................589
Mining customer preference ratings for product recommendation
using the support vector machine and the latent class model
W.K. Cheung, J.T. Kwok, M.H. Law&K-C. Tsui
............................................601
Data mining a large health insurance database
A.F. Gualtierotti
...............................................................................................611
Case study of a retail bank marketing datamart development
A. Cathie
...........................................................................................................619
Index of Authors
629
|
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| indexdate | 2024-07-09T18:45:03Z |
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| isbn | 185312821X |
| language | English |
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| spelling | Data mining II ed.: N. Ebecken ... Southampton [u.a.] WIT Press 2000 628 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Management information systems 2 Data Mining - Congrès Data mining gtt Data warehouse gtt Data mining Congresses Data Mining (DE-588)4428654-5 gnd rswk-swf (DE-588)1071861417 Konferenzschrift 2000 Cambridge gnd-content Data Mining (DE-588)4428654-5 s DE-604 Ebecken, N. Sonstige oth Management information systems 2 (DE-604)BV013392289 2 Digitalisierung TU Muenchen application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009135828&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| subject_GND | (DE-588)4428654-5 (DE-588)1071861417 |
| title | Data mining II |
| title_auth | Data mining II |
| title_exact_search | Data mining II |
| title_full | Data mining II ed.: N. Ebecken ... |
| title_fullStr | Data mining II ed.: N. Ebecken ... |
| title_full_unstemmed | Data mining II ed.: N. Ebecken ... |
| title_short | Data mining II |
| title_sort | data mining ii |
| topic | Data Mining - Congrès Data mining gtt Data warehouse gtt Data mining Congresses Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data Mining - Congrès Data mining Data warehouse Data mining Congresses Data Mining Konferenzschrift 2000 Cambridge |
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