Advances in Knowledge Discovery and Data Mining : 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16-18, 2001. Proceedings / edited by David Cheung, Graham J. Williams, Qing Li.

Format:

Book

Contributor:

Cheung, David, Ph. D., editor.

Williams, Graham J., editor.

Li, Qing, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 2035.

Lecture Notes in Artificial Intelligence ; 2035

Language:

English

Subjects (All):

Data structures (Computer science).

Artificial intelligence.

Information storage and retrieval.

Information technology.

Business--Data processing.

Business.

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.

Local Subjects:

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.

Physical Description:

1 online resource (XVII, 599 pages).

Edition:

First edition 2001.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2001.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-540-45357-4

9783540453574

Access Restriction:

Restricted for use by site license.