Data Mining: Foundations and Practice:
Saved in:
| Other Authors: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin [u.a.]
Springer
2008
|
| Series: | Studies in computational intelligence
118 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XV, 562 S. |
| ISBN: | 9783540784876 354078487X 9783540784883 |
Staff View
MARC
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Record in the Search Index
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|---|---|
| adam_text |
Contents
Compact
Representations of Sequential Classification Rules
Elena Baralis, Silvia
Chiusane,
Riccardo Dutto, and
Luigi
Mantellini
. 1
An Algorithm for Mining Weighted Dense Maximal
1-Complete Regions
Haiyun Bian and Raj Bhatnagar
. 31
Mining Linguistic Trends from Time Series
Chun-Hao Chen, Tzung-Pei Hong, and Vincent S. Tseng
. 49
Latent Semantic Space for Web Clustering
I-Jen Chiang, Tsau Young ('T. Y.') Lin, Hsiang-Chun Tsai,
Jau-Min Wong, and Xiaohua
Ни
. 61
A Logical Framework for Template Creation and Information
Extraction
David Corney, Emma Byrne, Bernard Buxton, and David Jones
. 79
A Bipolar Interpretation of Fuzzy Decision Trees
Tuan-Fang Fan, Churn-
Jung Hau,
and Duen-Ren Liu
.109
A Probability Theory Perspective on the Zadeh
Fuzzy System
Qing Shi
Gao,
Xiao Yu
Gao,
and Lei Xu
.125
Three Approaches to Missing Attribute Values: A Rough Set
Perspective
Jerzy W. Grzymala-Busse
.139
MLEM2 Rule Induction Algorithms: With and Without
Merging Intervals
Jerzy W.
Grzymala-Busse
.153
XIV Contents
Towards a Methodology for Data Mining Project
Development: The Importance of Abstraction
P. Gonzalez-Aranda, E.
Menasalvas,
S.
Millan,
Carlos Ruiz,
and J. Segovia
.165
Fining Active Membership Functions in Fuzzy Data Mining
Tzung-Pei Hong, Chun-Hao Chen, Yu-Lung Wu,
and Vincent S. Tseng
.179
A Compressed Vertical Binary Algorithm for Mining Frequent
Patterns
J. Hdez.
Palancar,
R.
Hdez.
León,
J.
Medina
Pagóla,
and
A. Hechavarría
.197
Naïve
Rules
Do Not Consider Underlying Causality
Lawrence J. Mazlack
.213
Inexact Multiple-Grained Causal Complexes
Lawrence J. Mazlack
.231
Does Relevance Matter to Data Mining Research?
Mykola Pechenizkiy, Seppo Puuronen, and Alexey Tsymbal
.251
E-Action Rules
Li-Shiang Tsay and
Zbigniew W.
Ras
.277
Mining
E-
Action Rules, System DEAR
Zbigniew W.
Ras
and
Li-Shiang Tsay.289
Definability of Association Rules and Tables of Critical
Frequencies
Jan Rauch.299
Classes of Association Rules: An Overview
Jan Rauch .315
Knowledge Extraction from Microarray
Datasets
Using Combined Multiple Models to Predict Leukemia Types
Gregor
Stiglic, Nawaz Khan, and Peter Kokol
.339
On the Complexity of the Privacy Problem in Databases
Bhavani Thuraisingham
.353
Ensembles of Least Squares Classifiers with Randomized
Kernels
Kari Torkkola
and Eugene Tuv
.375
On Pseudo-Statistical Independence in a Contingency Table
Shusaku Tsumoto
. 337
Contents
XV
Role of Sample Size and Determinants in Granularity
of Contingency Matrix
Shusaku Tsumoto
.405
Generating Concept Hierarchies from User Queries
Bob Wall, Neal
Richter,
and
Rafal
Angryk
.423
Mining Efficiently Significant Classification Association Rules
Yanbo J. Wang, Qin Xin, and
Frans Coenen.443
Data Preprocessing and Data Mining as Generalization
Anita Wasilewska and
Ernestina
Menasalvas.
469
Capturing Concepts and Detecting Concept-Drift from
Potential Unbounded, Ever-Evolving and High-Dimensional
Data Streams
Ying Xie, Ajay Ravichandran, Hisham Haddad,
and Katukuri Jayasimha
.485
A Conceptual Framework of Data Mining
Yiyu Yao,
Ning
Zhong, and Yan Zhao
.501
How to Prevent Private Data from being Disclosed
to a Malicious Attacker
Justin Zhan, LiWu Chang, and Stan Matwin
.517
Privacy-Preserving Naive Bayesian Classification
over Horizontally Partitioned Data
Justin Zhan, Stan Matwin, and LiWu Chang
.529
Using Association Rules for Classification from Databases
Having Class Label Ambiguities: A Belief Theoretic Method
S.P. Subasingha, J. Zhang, K. Premaratne, M.-L. Shyu, M. Kubat,
and K.K.R. G.K. Hewawasam
.539 |
| adam_txt |
Contents
Compact
Representations of Sequential Classification Rules
Elena Baralis, Silvia
Chiusane,
Riccardo Dutto, and
Luigi
Mantellini
. 1
An Algorithm for Mining Weighted Dense Maximal
1-Complete Regions
Haiyun Bian and Raj Bhatnagar
. 31
Mining Linguistic Trends from Time Series
Chun-Hao Chen, Tzung-Pei Hong, and Vincent S. Tseng
. 49
Latent Semantic Space for Web Clustering
I-Jen Chiang, Tsau Young ('T. Y.') Lin, Hsiang-Chun Tsai,
Jau-Min Wong, and Xiaohua
Ни
. 61
A Logical Framework for Template Creation and Information
Extraction
David Corney, Emma Byrne, Bernard Buxton, and David Jones
. 79
A Bipolar Interpretation of Fuzzy Decision Trees
Tuan-Fang Fan, Churn-
Jung Hau,
and Duen-Ren Liu
.109
A Probability Theory Perspective on the Zadeh
Fuzzy System
Qing Shi
Gao,
Xiao Yu
Gao,
and Lei Xu
.125
Three Approaches to Missing Attribute Values: A Rough Set
Perspective
Jerzy W. Grzymala-Busse
.139
MLEM2 Rule Induction Algorithms: With and Without
Merging Intervals
Jerzy W.
Grzymala-Busse
.153
XIV Contents
Towards a Methodology for Data Mining Project
Development: The Importance of Abstraction
P. Gonzalez-Aranda, E.
Menasalvas,
S.
Millan,
Carlos Ruiz,
and J. Segovia
.165
Fining Active Membership Functions in Fuzzy Data Mining
Tzung-Pei Hong, Chun-Hao Chen, Yu-Lung Wu,
and Vincent S. Tseng
.179
A Compressed Vertical Binary Algorithm for Mining Frequent
Patterns
J. Hdez.
Palancar,
R.
Hdez.
León,
J.
Medina
Pagóla,
and
A. Hechavarría
.197
Naïve
Rules
Do Not Consider Underlying Causality
Lawrence J. Mazlack
.213
Inexact Multiple-Grained Causal Complexes
Lawrence J. Mazlack
.231
Does Relevance Matter to Data Mining Research?
Mykola Pechenizkiy, Seppo Puuronen, and Alexey Tsymbal
.251
E-Action Rules
Li-Shiang Tsay and
Zbigniew W.
Ras
.277
Mining
E-
Action Rules, System DEAR
Zbigniew W.
Ras
and
Li-Shiang Tsay.289
Definability of Association Rules and Tables of Critical
Frequencies
Jan Rauch.299
Classes of Association Rules: An Overview
Jan Rauch .315
Knowledge Extraction from Microarray
Datasets
Using Combined Multiple Models to Predict Leukemia Types
Gregor
Stiglic, Nawaz Khan, and Peter Kokol
.339
On the Complexity of the Privacy Problem in Databases
Bhavani Thuraisingham
.353
Ensembles of Least Squares Classifiers with Randomized
Kernels
Kari Torkkola
and Eugene Tuv
.375
On Pseudo-Statistical Independence in a Contingency Table
Shusaku Tsumoto
. 337
Contents
XV
Role of Sample Size and Determinants in Granularity
of Contingency Matrix
Shusaku Tsumoto
.405
Generating Concept Hierarchies from User Queries
Bob Wall, Neal
Richter,
and
Rafal
Angryk
.423
Mining Efficiently Significant Classification Association Rules
Yanbo J. Wang, Qin Xin, and
Frans Coenen.443
Data Preprocessing and Data Mining as Generalization
Anita Wasilewska and
Ernestina
Menasalvas.
469
Capturing Concepts and Detecting Concept-Drift from
Potential Unbounded, Ever-Evolving and High-Dimensional
Data Streams
Ying Xie, Ajay Ravichandran, Hisham Haddad,
and Katukuri Jayasimha
.485
A Conceptual Framework of Data Mining
Yiyu Yao,
Ning
Zhong, and Yan Zhao
.501
How to Prevent Private Data from being Disclosed
to a Malicious Attacker
Justin Zhan, LiWu Chang, and Stan Matwin
.517
Privacy-Preserving Naive Bayesian Classification
over Horizontally Partitioned Data
Justin Zhan, Stan Matwin, and LiWu Chang
.529
Using Association Rules for Classification from Databases
Having Class Label Ambiguities: A Belief Theoretic Method
S.P. Subasingha, J. Zhang, K. Premaratne, M.-L. Shyu, M. Kubat,
and K.K.R. G.K. Hewawasam
.539 |
| any_adam_object | 1 |
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| dewey-ones | 006 - Special computer methods |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| discipline_str_mv | Informatik |
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| id | DE-604.BV023376165 |
| illustrated | Not Illustrated |
| index_date | 2024-07-02T21:14:37Z |
| indexdate | 2024-08-20T00:33:33Z |
| institution | BVB |
| isbn | 9783540784876 354078487X 9783540784883 |
| language | English |
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| physical | XV, 562 S. |
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| publisher | Springer |
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| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spelling | Data Mining: Foundations and Practice Tsau Young Lin ... (Eds.) Berlin [u.a.] Springer 2008 XV, 562 S. txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 118 Data Mining (DE-588)4428654-5 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Data Mining (DE-588)4428654-5 s DE-604 Lin, Tsau Young 1937- (DE-588)123587352 edt Studies in computational intelligence 118 (DE-604)BV020822171 118 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016559343&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Data Mining: Foundations and Practice Studies in computational intelligence Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4143413-4 |
| title | Data Mining: Foundations and Practice |
| title_auth | Data Mining: Foundations and Practice |
| title_exact_search | Data Mining: Foundations and Practice |
| title_exact_search_txtP | Data Mining: Foundations and Practice |
| title_full | Data Mining: Foundations and Practice Tsau Young Lin ... (Eds.) |
| title_fullStr | Data Mining: Foundations and Practice Tsau Young Lin ... (Eds.) |
| title_full_unstemmed | Data Mining: Foundations and Practice Tsau Young Lin ... (Eds.) |
| title_short | Data Mining: Foundations and Practice |
| title_sort | data mining foundations and practice |
| topic | Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data Mining Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016559343&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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