Active Mining - New Directions Of Data Mining
by Hiroshi MotodaThe need for collecting relevant data sources, mining useful knowledge from different forms of data sources and promptly reacting to situation change is ever increasing. Active mining is a collection of activities each solving a part of this need, but collectively achieving the mining objective through the spiral effect of these interleaving three steps. This book is… See more details below
Overview
The need for collecting relevant data sources, mining useful knowledge from different forms of data sources and promptly reacting to situation change is ever increasing. Active mining is a collection of activities each solving a part of this need, but collectively achieving the mining objective through the spiral effect of these interleaving three steps. This book is a joint effort from leading and active researchers in Japan with a theme about active mining and a timely report on the forefront of data collection, user-centered mining and user interaction/reaction. It offers a contemporary overview of modern solutions with real-world applications, shares hard-learned experiences, and sheds light on future development of active mining.
Product Details
- ISBN-13:
- 9781586032647
- Publisher:
- I O S Press, Incorporated
- Publication date:
- 07/29/2002
- Series:
- Frontiers in Artificial Intelligence and Applications Series
- Pages:
- 304
- Product dimensions:
- 6.14(w) x 9.21(h) x 0.69(d)
Table of Contents
| Preface | ||
| Acknowledgments | ||
| Toward Active Mining from On-line Scientific Text Abstracts Using Pre-existing Sources | 3 | |
| Data Mining on the WAVEs - Word-of-mouth-Assisting Virtual Environments | 11 | |
| Immune Network-based Clustering for WWW Information Gathering/Visualization | 21 | |
| Interactive Web Page Retrieval with Relational Learning-based Filtering Rules | 31 | |
| Monitoring Partial Update of Web Pages by Interactive Relational Learning | 41 | |
| Context-based Classification of Technical Terms Using Support Vector Machines | 51 | |
| Intelligent Tickers: An Information Integration Scheme for Active Information Gathering | 61 | |
| Discovery of Concept Relation Rules Using an Incomplete Key Concept Dictionary | 73 | |
| Mining Frequent Substructures from Web | 83 | |
| Towards the Discovery of Web Communities from Input Keywords to a Search Engine | 95 | |
| Temporal Spatial Index Techniques for OLAP in Traffic Data Warehouse | 103 | |
| Knowledge Discovery from Structured Data by Beam-wise Graph-Based Induction | 115 | |
| PAGA Discovery: A Worst-Case Analysis of Rule Discovery for Active Mining | 127 | |
| Evaluating the Automatic Composition of Inductive Applications Using StatLog Repository of Data Set | 139 | |
| Fast Boosting Based on Iterative Data Squashing | 151 | |
| Reducing Crossovers in Reconciliation Graphs Using the Coupling Cluster Exchange Method with a Genetic Algorithm | 163 | |
| Outlier Detection using Cluster Discriminant Analysis | 175 | |
| Evidence-Based Medicine and Data Mining: Developing a Causal Model via Meta-Learning Methodology | 187 | |
| KeyGraph for Classifying Web Communities | 195 | |
| Case Generation Method for Constructing an RDR Knowledge Base | 205 | |
| Acquiring Knowledge from Both Human Experts and Accumulated Data in an Unstable Environment | 217 | |
| Active Participation of Users with Visualization Tools in the Knowledge Discovery Process | 229 | |
| The Future Direction of Active Mining in the Business World | 239 | |
| Topographical Expression of a Rule for Active Mining | 247 | |
| The Effect of Spatial Representation of Information on Decision Making in Purchase | 259 | |
| A Hybrid Approach of Multiscale Matching and Rough Clustering to Knowledge Discovery in Temporal Medical Databases | 269 | |
| Meta Analysis for Data Mining | 279 | |
| Author Index | 291 |
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