Rough Sets, Fuzzy Sets, Data Mining and Granular Computing: 11th International Conference, RSFDGrC 2007, Toronto, Canada, May 14-16, 2007Aijun An, Jerzy Stefanowski, Sheela Ramanna, Cory Butz, Witold Pedrycz Springer, 2007. gada 22. aug. - 588 lappuses This volume contains the papers selected for presentation at the 11th Int- national Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2007), a part of the Joint Rough Set Symposium (JRS 2007) organized by Infobright Inc. and York University. JRS 2007 was held for the ?rst time during May 14–16, 2007 in MaRS Discovery District, Toronto, Canada. It consisted of two conferences: RSFDGrC 2007 and the Second Int- national Conference on Rough Sets and Knowledge Technology (RSKT 2007). The two conferences that constituted JRS 2007 investigated rough sets as an emerging methodology established more than 25 years ago by Zdzis law Pawlak. Roughsettheoryhasbecomeanintegralpartofdiversehybridresearchstreams. In keeping with this trend, JRS 2007 encompassed rough and fuzzy sets, kno- edgetechnologyanddiscovery,softandgranularcomputing,dataprocessingand mining, while maintaining an emphasis on foundations and applications. RSFDGrC 2007 followed in the footsteps of well-established international initiatives devoted to the dissemination of rough sets research, held so far in Canada, China, Japan, Poland, Sweden, and the USA. RSFDGrC was ?rst - ganized as the 7th International Workshop on Rough Sets, Data Mining and Granular Computing held in Yamaguchi, Japan in 1999. Its key feature was to stress the role of integrating intelligent information methods to solve real-world, large, complex problems concerned with uncertainty and fuzziness. RSFDGrC achieved the status of a bi-annual international conference, starting from 2003 in Chongqing, China. |
No grāmatas satura
1.–5. rezultāts no 68.
v. lappuse
... Pawlak. Rough set theory has become an integral part of diverse hybrid research streams. In keeping with this trend, JRS 2007 encompassed rough and fuzzy sets, knowledge technology and discovery, soft and granular computing, data ...
... Pawlak. Rough set theory has become an integral part of diverse hybrid research streams. In keeping with this trend, JRS 2007 encompassed rough and fuzzy sets, knowledge technology and discovery, soft and granular computing, data ...
vii. lappuse
... Pawlak Leonid Perlovsky Georg Peters Fred Petry Bhanu Prasad Leszek Rutkowski Hiroshi Sakai B. Uma Shankar Arul Siromoney Jaroslaw Stepaniuk Andrzej Szalas Ruppa Thulasiram I. Burhan Turksen Gwo-Hshiung Tzeng Dimiter Vakarelov Lipo Wang ...
... Pawlak Leonid Perlovsky Georg Peters Fred Petry Bhanu Prasad Leszek Rutkowski Hiroshi Sakai B. Uma Shankar Arul Siromoney Jaroslaw Stepaniuk Andrzej Szalas Ruppa Thulasiram I. Burhan Turksen Gwo-Hshiung Tzeng Dimiter Vakarelov Lipo Wang ...
2. lappuse
... Pawlak [43,44,45,46,17] is a mathematical approach to imperfect knowledge. The problem of imperfect knowledge has been tackled for a long time by philosophers, logicians and mathematicians. Recently it became also a crucial issue for ...
... Pawlak [43,44,45,46,17] is a mathematical approach to imperfect knowledge. The problem of imperfect knowledge has been tackled for a long time by philosophers, logicians and mathematicians. Recently it became also a crucial issue for ...
7. lappuse
... Pawlak (see [43]) and their students. In some sense, it is a continuation of ideas initiated by Leibniz, Boole and currently continued in a variety of forms. Of course, the Rasiowa - Pawlak school is also some kind continuation of the ...
... Pawlak (see [43]) and their students. In some sense, it is a continuation of ideas initiated by Leibniz, Boole and currently continued in a variety of forms. Of course, the Rasiowa - Pawlak school is also some kind continuation of the ...
8. lappuse
... Pawlak [43], developed in the Rasiowa-Pawlak school is based on classical two valued logic. The rough set approach has been developed to deal with uncertainty and vagueness. The approach makes it possible to reason precisely about ...
... Pawlak [43], developed in the Rasiowa-Pawlak school is based on classical two valued logic. The rough set approach has been developed to deal with uncertainty and vagueness. The approach makes it possible to reason precisely about ...
Saturs
1 | |
13 | |
25 | |
37 | |
47 | |
Fuzzy Approximation Operators Based on Coverings | 55 |
InformationTheoretic Measure of Uncertainty in Generalized Fuzzy | 63 |
Determining Significance of Attributes in the Unified Rough | 71 |
Evaluating Importance of Conditions in the Set of Discovered Rules Salvatore Greco Roman Slowinski and Jerzy Stefanowski | 314 |
Constraint Based Action Rule Discovery with Single Classification | 322 |
Data Confidentiality Versus Chase Zbigniew W Ras Osman Gürdal Seunghyun Im and | 330 |
Relationship Between Loss Functions and Confirmation Measures Krzysztof Dembczynski Salvatore Greco Wojciech Kotlowski and | 338 |
High Frequent Value Reduct in Very Large Databases Tsau Young Lin and Jianchao Han | 346 |
A Weighted Rough Set Approach for CostSensitive Learning Jinfu Liu and Daren Yu | 355 |
Jumping Emerging Pattern Induction by Means of Graph Coloring | 363 |
Visualization of Rough Set Decision Rules for Medical Diagnosis | 371 |
A RoughHybrid Approach to Software Defect Classification | 79 |
Vaguely Quantified Rough Sets | 87 |
A Fuzzy Search Engine Weighted Approach to Result Merging | 95 |
A Fuzzy Group Decision Approach to Real Option Valuation | 103 |
Fuzzifying Branimir Closure Seˇseljaˇ Systems and Fuzzy Lattices 111 and Andreja Tepavcevic | 119 |
A New Classifier Design with Fuzzy Functions | 136 |
Nucleus Segmentation and Recognition of Uterine Cervical | 153 |
Multiscale Extraction of Hypodense Signs | 171 |
Ordinal Credibility Coefficient A New Approach in the Data | 190 |
Parallel Artificial Immune Clustering Algorithm Based on Granular | 208 |
DensityBased Clustering with Constraints Carlos Ruiz Myra Spiliopoulou and Ernestina Menasalvas | 216 |
A New Cluster Based Fuzzy Model Tree for Data Modeling DaeJong Lee SangYoung Park NahmChung Jung and | 224 |
Parameter Tuning for Disjoint Clusters Based on Concept Lattices | 232 |
Text and Web Mining | 240 |
Transformation of Suffix Arrays into Suffix Trees on the | 248 |
Clustering High Dimensional Data Using SVM Tsau Young Lin and Tam Ngo | 256 |
Learning Data Mining and Rough Classifiers | 263 |
Evaluation Method for Decision Rule Sets Yuhua Qian and Jiye Liang | 272 |
On Possible Rules and Apriori Algorithm in Nondeterministic | 280 |
Neonatal Infection Diagnosis Using Constructive Induction in Data | 289 |
Two Families of Classification Algorithms | 297 |
Constructing Associative Classifiers from Decision Tables Jianchao Han T Y Lin Jiye Li and Nick Cercone | 305 |
Attribute Generalization and Fuzziness in Data Mining Contexts Shusaku Tsumoto | 379 |
A Hybrid Method for Forecasting Stock Market Trend Using | 387 |
Granular Computing | 395 |
An Incremental Updating Algorithm for Core Computing | 403 |
A Ranking Approach with Inclusion Measure in MultipleAttribute | 411 |
Granulations Based on Semantics of Rough Logical Formulas and | 419 |
A Categorial Basis for Granular Computing | 427 |
Granular Dominik Sets Slezak Foundations and Piotr and Wasilewski Case Study of Tolerance Spaces | 435 |
Soft Computing in Multimedia Processing | 443 |
TaskBased Image Annotation and Retrieval | 451 |
Improvement of Moving Image Quality on ACPDP by Rough | 459 |
Image Digital Watermarking Technique Based on Kernel Independent | 467 |
Image Pattern Recognition Using Near Sets | 475 |
Robotic Target Tracking with Approximation SpaceBased Feedback | 483 |
Soft Computing Applications | 490 |
Application | 500 |
Intelligent Decision Support Based on Influence Diagrams with Rough | 518 |
Coverage in Biomimetic Pattern Recognition | 534 |
Rough and Complex Concepts | 550 |
Description Logic Framework for Access Control and Security | 565 |
Author Index | 583 |
Citi izdevumi - Skatīt visu
Rough Sets, Fuzzy Sets, Data Mining and Granular Computing: 11th ... Aijun An,Jerzy Stefanowski,Sheela Ramanna,Cory Butz,Witold Pedrycz Ierobežota priekšskatīšana - 2007 |
Bieži izmantoti vārdi un frāzes
action rules analysis applications approach approximation space association rules attribute reduction attribute values Berlin Heidelberg 2007 classification closure system complete lattice concept concept lattice condition attributes credibility coefficients crisp data mining data set database decision attribute decision rules decision table decision trees defined Definition denoted discernibility matrix document domain evaluation example extracted feature fuzzy clustering fuzzy partition fuzzy relation fuzzy rough sets fuzzy sets Fuzzy Systems Gaussian smoothed image Granular Computing granules Grid hypodense IEEE influence diagrams information system input K-means kernel knowledge discovery lattice LNAI measure membership values method neural networks node objects optimal output paper parameter pattern Pawlak pixels problem proposed query retrieval rough set theory RSFDGrC samples search engine Section segmentation selected Skowron Soft Computing Springer-Verlag Berlin Heidelberg subset suffix arrays Support Vector Machines t-norm techniques Theorem threshold upper approximation weights