Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving

Authors:
Nada Lavrač/;Hiroshi Motoda;Tom Fawcett;Robert Holte;Pat Langley;Pieter Adriaans
Affiliations:
Institute Jož/ef Stefan, Jamova 39, 1000 Ljubljana, Slovenia/ Nova Gorica Polytechnic, Vipavska 13, 5000 Nova Gorica, Slovenia. nada.lavrac@ijs.si;Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan. motoda@sanken.osaka-u.ac.jp;Hewlett-Packard Labs, 1501 Page Mill Rd., Palo Alto, CA 94304, USA. tom.fawcett@hp.com;Computing Science Department, University of Alberta, Edmonton, Alberta Canada T6G 2E8. holte@cs.ualberta.ca;Computational Learning Laboratory, Center for the Study of Language &/ Information, Stanford University, Stanford, CA 94305, USA. langley@csli.stanford.edu;Institute for Language Logic and Computation, Plantage Muidergracht 24, 1018 TV, Amsterdam, The Netherlands. pietera@science.uva.nl
Venue:
Machine Learning
Year:
2004

Citing 24
Cited 5

Structured induction in expert systems

Structured induction in expert systems
Concept learning and heuristic classification in weak-theory domains

Artificial Intelligence
Applications of machine learning and rule induction

Communications of the ACM
Data mining

Data mining
From data mining to knowledge discovery: an overview

Advances in knowledge discovery and data mining
Learning in the “Real World”

Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Machine Learning for the Detection of Oil Spills in Satellite Radar Images

Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Data preparation for data mining

Data preparation for data mining
The agile virtual enterprise: cases, metrics, tools

The agile virtual enterprise: cases, metrics, tools
The computational support of scientific discovery

International Journal of Human-Computer Studies - Special issue on Machine Discovery
Function point analysis: measurement practices for successful software projects

Function point analysis: measurement practices for successful software projects
Principles of data mining

Principles of data mining
Ontology Learning for the Semantic Web

Ontology Learning for the Semantic Web
Decision Support Systems and Intelligent Systems

Decision Support Systems and Intelligent Systems
Relational Data Mining

Relational Data Mining
Data Mining Techniques: For Marketing, Sales, and Customer Support

Data Mining Techniques: For Marketing, Sales, and Customer Support
Knowledge discovery from data?

IEEE Intelligent Systems
Guest Editor's Introduction: Knowledge Management and the Internet

IEEE Intelligent Systems
The Case against Accuracy Estimation for Comparing Induction Algorithms

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Computational Revision of Quantitative Scientific Models

DS '01 Proceedings of the 4th International Conference on Discovery Science
Production control

Handbook of data mining and knowledge discovery
Industry: using decision tree induction to minimize process delays in the printing industry

Handbook of data mining and knowledge discovery
Industry: telecommunications network diagnosis

Handbook of data mining and knowledge discovery
Lessons and Challenges from Mining Retail E-Commerce Data

Machine Learning

Editorial: Data Mining Lessons Learned

Machine Learning
Lamb Meat Quality Assessment by Support Vector Machines

Neural Processing Letters
Toward data mining engineering: A software engineering approach

Information Systems
Mortality assessment in intensive care units via adverse events using artificial neural networks

Artificial Intelligence in Medicine
BI and Data Warehouse Solutions for Energy Production Industry: Application of the CRISP-DM methodology

Proceedings of the 2010 conference on Bridging the Socio-technical Gap in Decision Support Systems: Challenges for the Next Decade

Quantified Score

Hi-index	0.00

Visualization

Abstract

This introductory paper to the special issue on Data Mining Lessons Learned presents lessons from data mining applications, including experience from science, business, and knowledge management in a collaborative data mining setting.