Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
Instance-Based Classification by Emerging Patterns
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Information-Based Classification by Aggregating Emerging Patterns
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Making Use of the Most Expressive Jumping Emerging Patterns for Classification
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Combining the Strength of Pattern Frequency and Distance for Classification
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
An Efficient Single-Scan Algorithm for Mining Essential Jumping Emerging Patterns for Classification
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Fast Algorithms for Mining Emerging Patterns
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
Incremental Maintenance on the Border of the Space of Emerging Patterns
Data Mining and Knowledge Discovery
Using Emerging Patterns and Decision Trees in Rare-Class Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
Using Emerging Patterns to Construct Weighted Decision Trees
IEEE Transactions on Knowledge and Data Engineering
World Wide Web
Efficient Mining of Contrast Patterns and Their Applications to Classification
ICISIP '05 Proceedings of the 2005 3rd International Conference on Intelligent Sensing and Information Processing
Further improving emerging pattern based classifiers via bagging
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Exploiting maximal emerging patterns for classification
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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The ability to distinguish, differentiate and contrast between different data sets is a key objective in data mining. Such ability can assist domain experts to understand their data, and can help in building classification models. This presentation will introduce the principal techniques for contrasting data sets. It will also focus on some important real world application areas that illustrate how mining contrasts is advantageous.