Advanced Data Mining and Applications: First International Conference, ADMA 2005, Wuhan, China, July 22-24, 2005, ProceedingsSpringer Science & Business Media, 12 Ιουλ 2005 - 835 σελίδες With the ever-growing power to generate, transmit and collect huge amounts of data, information overload is now an imminent problem to mankind. The overwhelming demand for information processing is not just about a better - derstanding of data, but also a better usage of data in a timely fashion. Data mining, or knowledge discovery from databases, is proposed to gain insight into aspects of dataand to help peoplemakeinformed, sensible, andbetter decisions. At present, growing attention has been paid to the study, development and - plication of data mining. As a result there is an urgent need for sophisticated techniques and tools that can handle new ?elds of data mining, e.g., spatialdata mining, biomedical data mining, and mining on high-speed and time-variant data streams. The knowledge of data mining should also be expanded to new applications. The1stInternationalConferenceonAdvancedDataMiningandApplications (ADMA 2005) aimed to bring together the experts on data mining throughout the world. It provided a leading international forum for the dissemination of original research results in advanced data mining techniques, applications, al- rithms, software and systems, and di'erent applied disciplines. The conference attracted 539 online submissions and 63 mailing submissions from 25 di'erent countriesandareas.Allfullpaperswerepeer reviewedbyatleastthreemembers of the Program Committee composed of international experts in data mining ?elds. A total number of 100 papers were accepted for the conference. Amongst them 25 papers were selected as regular papers and 75 papers were selected as short papers, yielding a combined acceptance rate of 17%. |
Περιεχόμενα
Keynote Papers | 1 |
Relevance of Counting in Data Mining Tasks | 14 |
A Latent Usage Approach for Clustering Web Transaction and Building | 31 |
An Approach to Mining Local Causal Relationships from Databases | 51 |
Finding All Frequent Patterns Starting from the Closure | 67 |
A New Technique for Visualizing Mined Association Rules | 88 |
Classification | 108 |
Mining Correlated Rules for Associative Classification | 130 |
Automatic Image Registration via Clustering and Convex Hull Vertices | 439 |
Sequential Data Mining and Time Series Mining | 457 |
Independent Component Analysis for Clustering Multivariate Time | 474 |
Web Mining | 491 |
A Novel Framework for Web Page Classification Using TwoStage | 499 |
Querying Web Images by Topic and Example Specification Methods | 515 |
The Research on Fuzzy Data Mining Applied on Browser Records | 529 |
Biomedical Mining | 544 |
Using Latent Class Models for Neighbors Selection in Collaborative | 149 |
Reducts in Incomplete Decision Tables | 165 |
One Dependence Augmented Naive Bayes | 186 |
A Fast Fuzzy Clustering Algorithm for LargeScale Datasets | 203 |
Extracting the Representative Failure Executions via Clustering | 217 |
Optimal Fuzzy Modeling Based on Minimum Cluster Volume | 232 |
Clustering Categorical Data Using Coverage Density | 248 |
The Infinite Polynomial Kernel for Support Vector Machine | 267 |
The Application of Adaptive Partitioned Random Search in Feature | 277 |
A Novel Data Mining Method Based on Ant Colony Algorithm | 284 |
Customer Churn Prediction Using Improved OneClass Support Vector | 300 |
Heuristic Scheduling of Concurrent Data Mining Queries | 315 |
Iteration Method | 333 |
A Study on Text Clustering Algorithms Based on Frequent Term Sets | 347 |
Word Segmentation and POS Tagging for Chinese Keyphrase Extraction | 364 |
Multimedia Mining | 382 |
An Approach to Compressed Image Retrieval Based on JPEG2000 | 398 |
ART in Image Reconstruction with Narrow FanBeam Based on Data | 407 |
A Novel Information Hiding Technique for Remote Sensing Image | 423 |
Robust Ensemble Learning for Cancer Diagnosis Based on Microarray | 564 |
An Analysis of Missing Data Treatment Methods and Their Application | 583 |
Mining Interesting Association Rules in Medical Images | 598 |
Predicting Subcellular Localization of Proteins Using Support Vector | 618 |
Using Boosting Learning Method for Intrusion Detection | 634 |
Automatic Inspection of Industrial Sheetmetal Parts with Single | 654 |
Structural Damage Detection by Integrating Independent Component | 670 |
Classifying Class and Finding Community in UML Metamodel | 690 |
An Adaptive Network Intrusion Detection Method Based on PCA | 696 |
Dynamic Shape Modeling of Consumers Daily Load Based on Data | 712 |
A BP Neural Network Predictor Model for Desulfurizing Molten Iron | 728 |
Security and Privacy Issues | 744 |
Spatial Data Mining | 761 |
Spatial Information Multigrid for Data Mining | 777 |
A Uniform Framework of 3D Spatial Data Model and Data Mining | 785 |
Mining Recent Frequent Itemsets in Data Streams by Radioactively | 804 |
A GridBased Clustering Algorithm for HighDimensional Data Streams | 824 |
Άλλες εκδόσεις - Προβολή όλων
Advanced Data Mining and Applications: First International Conference, ADMA ... Xue Li,Shuliang Wang,Zhao Yang Dong Περιορισμένη προεπισκόπηση - 2003 |
Advanced Data Mining and Applications: First International Conference, ADMA ... Xue Li,Shuliang Wang Δεν υπάρχει διαθέσιμη προεπισκόπηση - 2005 |
Advanced Data Mining and Applications: First International Conference, ADMA ... Xue Li,Shuliang Wang,Zhao Yang Dong Δεν υπάρχει διαθέσιμη προεπισκόπηση - 2005 |
Συχνά εμφανιζόμενοι όροι και φράσεις
accuracy ADMA analysis applications approach association rules attributes Berlin Heidelberg 2005 bit-planes China classifier clustering algorithm collaborative filtering Computer Science data mining data mining queries data streams database dataset decision tables decision trees defined denotes distribution documents evaluate example experimental results experiments extract frequent itemsets function fuzzy graph grid Heidelberg IEEE improve information hiding input International Conference intrusion detection itemsets iteration K-means kernel Knowledge Discovery LDAP LNAI LonWorks Machine Learning matrix method minimum support naive Bayes neural network node object optimal output paper parameters partition pattern performance points prediction problem proposed protein Q-learning region retrieval segmentation sequence server similar spatial data Springer-Verlag Berlin Heidelberg step structure subset subtrees support vector machine Table techniques Technology threshold transactions Wang weight Wuhan University Z.Y. Dong Eds