Advances in Knowledge Discovery and Data Mining 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16-18, 2001. Proceedings /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Cheung, David (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Williams, Graham J. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Li, Qing (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2001.
• Έκδοση: 1st ed. 2001.
• Σειρά: Lecture Notes in Artificial Intelligence ; 2035
• Θέματα: Data structures (Computer science).; Artificial intelligence.; Information storage and retrieval.; Information technology.; Business-Data processing.; Application software.; Mathematical statistics.; Data Structures and Information Theory.; Artificial Intelligence.; Information Storage and Retrieval.; IT in Business.; Information Systems Applications (incl. Internet).; Probability and Statistics in Computer Science.

 This book constitutes the refereed proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001, held in Hong Kong, China in April 2001. The 38 revised full papers and 22 short papers presented were carefully reviewed and selected from a total of 152 submissions. The book offers topical sections on Web mining, text mining, applications and tools, concept hierarchies, feature selection, interestingness, sequence mining, spatial and temporal mining, association mining, classification and rule induction, clustering, and advanced topics and new methods. 

 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.