Cheung / Williams / Li - Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining

5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16-18, 2001. Proceedings

Medium: Buch

ISBN: 978-3-540-41910-5

Verlag: Springer

Erscheinungstermin: 04.04.2001

106,99 € 99,99 € (zzgl. MwSt.)

Lieferfrist: bis zu 10 Tage

Keynote Presentations.- Incompleteness in Data Mining.- Mining E-Commerce Data: The Good, the Bad, and the Ugly.- Seamless Integration of Data Mining with DBMS and Applications.- Web Mining.- Applying Pattern Mining to Web Information Extraction.- Empirical Study of Recommender Systems Using Linear Classifiers.- iJADE eMiner - A Web-Based Mining Agent Based on Intelligent Java Agent Development Environment (iJADE) on Internet Shopping.- A Characterized Rating Recommend System.- Discovery of Frequent Tree Structured Patterns in Semistructured Web Documents.- Text Mining.- Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification.- Predictive Self-Organizing Networks for Text Categorization.- Meta-learning Models for Automatic Textual Document Categorization.- Efficient Algorithms for Concept Space Construction.- Topic Detection, Tracking, and Trend Analysis Using Self-Organizing Neural Networks.- Automatic Hypertext Construction through a Text Mining Approach by Self-Organizing Maps.- Applications and Tools.- Semantic Expectation-Based Causation Knowledge Extraction: A Study on Hong Kong Stock Movement Analysis.- A Toolbox Approach to Flexible and Efficient Data Mining.- Determining Progression in Glaucoma Using Visual Fields.- Seabreeze Prediction Using Bayesian Networks.- Semi-supervised Learning in Medical Image Database.- On Application of Rough Data Mining Methods to Automatic Construction of Student Models.- Concept Hierarchies.- Concept Approximation in Concept Lattice.- Generating Concept Hierarchies/Networks: Mining Additional Semantics in Relational Data.- Representing Large Concept Hierarchies Using Lattice Data Structure.- Feature Selection.- Feature Selection for Temporal Health Records.- Boosting the Performance of Nearest Neighbour Methods with Feature Selection.- Feature Selection for Meta-learning.- Interestingness.- Efficient Mining of Niches and Set Routines.- Evaluation of Interestingness Measures for Ranking Discovered Knowledge.- Peculiarity Oriented Mining and Its Application for Knowledge Discovery in Amino-Acid Data.- Sequence Mining.- Mining Sequence Patterns from Wind Tunnel Experimental Data for Flight Control.- Scalable Hierarchical Clustering Method for Sequences of Categorical Values.- FFS - An I/O-Efficient Algorithm for Mining Frequent Sequences.- Sequential Index Structure for Content-Based Retrieval.- Spatial and Temporal Mining.- The S 2-Tree: An Index Structure for Subsequence Matching of Spatial Objects.- Temporal Data Mining Using Hidden Markov-Local Polynomial Models.- Patterns Discovery Based on Time-Series Decomposition.- Criteria on Proximity Graphs for Boundary Extraction and Spatial Clustering.- Micro Similarity Queries in Time Series Database.- Association Mining.- Mining Optimal Class Association Rule Set.- Generating Frequent Patterns with the Frequent Pattern List.- User-Defined Association Mining.- Direct and Incremental Computing of Maximal Covering Rules.- Towards Efficient Data Re-mining (DRM).- Data Allocation Algorithm for Parallel Association Rule Discovery.- Classification and Rule Induction.- Direct Domain Knowledge Inclusion in the PA3 Rule Induction Algorithm.- Hierarchical Classification of Documents with Error Control.- An Efficient Data Compression Approach to the Classification Task.- Combining the Strength of Pattern Frequency and Distance for Classification.- A Scalable Algorithm for Rule Post-pruning of Large Decision Trees.- Optimizing the Induction of Alternating Decision Trees.- Building Behaviour Knowledge Space to Make Classification Decision.- Clustering.- Efficient Hierarchical Clustering Algorithms Using Partially Overlapping Partitions.- A Rough Set-Based Clustering Method with Modification of Equivalence Relations.- Importance of Individual Variables in the k-Means Algorithm.- A Hybrid Approach to Clustering in Very Large Databases.- Advanced Topics and New Methods.- A Similarity Indexing Method for the Data Warehousing - Bit-Wise Indexing Method.- Rule Reduction over Numerical Attributes in Decision Trees Using Multilayer Perceptron.- Knowledge Acquisition from Both Human Expert and Data.- Neighborhood Dependencies for Prediction.- Learning Bayesian Networks with Hidden Variables Using the Combination of EM and Evolutionary Algorithms.- Interactive Construction of Decision Trees.- An Improved Learning Algorithm for Augmented Naive Bayes.- Generalised RBF Networks Trained Using an IBL Algorithm for Mining Symbolic Data.