Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Communications of the ACM
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Efficient discovery of error-tolerant frequent itemsets in high dimensions
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Machine Learning
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Machine Learning
The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
LPMiner: An Algorithm for Finding Frequent Itemsets Using Length-Decreasing Support Constraint
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Efficient Mining of High Confidience Association Rules without Support Thresholds
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
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
Incremental Maintenance on the Border of the Space of Emerging Patterns
Data Mining and Knowledge Discovery
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
World Wide Web
Mining statistically important equivalence classes and delta-discriminative emerging patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Jumping emerging patterns with negation in transaction databases - Classification and discovery
Information Sciences: an International Journal
Mining decision rules on data streams in the presence of concept drifts
Expert Systems with Applications: An International Journal
Efficient Mining of Jumping Emerging Patterns with Occurrence Counts for Classification
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Efficient Discovery of Top-K Minimal Jumping Emerging Patterns
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
A novel distance-based classifier built on pattern ranking
Proceedings of the 2009 ACM symposium on Applied Computing
Diverging patterns: discovering significant frequency change dissimilarities in large databases
Proceedings of the 18th ACM conference on Information and knowledge management
Feature construction based on closedness properties is not that simple
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Attribute set dependence in reduct computation
Transactions on computational science II
Functional proteomic pattern identification under low dose ionizing radiation
Artificial Intelligence in Medicine
Contrast pattern mining and its applications
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Transactions on rough sets XII
An approach for adaptive associative classification
Expert Systems with Applications: An International Journal
Efficient mining of jumping emerging patterns with occurrence counts for classification
Transactions on rough sets XIII
Cascading an emerging pattern based classifier
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
A framework to mine high-level emerging patterns by attribute-oriented induction
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
A new emerging pattern mining algorithm and its application in supervised classification
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Mining emerging patterns by streaming feature selection
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
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Classification of large data sets is an important data mining problem that has wide applications. Jumping Emerging Patterns (JEPs) are those itemsets whose supports increase abruptly from zero in one data set to nonzero in another data set. In this paper, we propose a fast, accurate, and less complex classifier based on a subset of JEPs, called Strong Jumping Emerging Patterns (SJEPs). The support constraint of SJEP removes potentially less useful JEPs while retaining those with high discriminating power. Previous algorithms based on the manipulation of border [1] as well as consEPMiner [2] cannot directly mine SJEPs. Here, we present a new tree-based algorithm for their efficient discovery. Experimental results show that: 1) the training of our classifier is typically 10 times faster than earlier approaches, 2) our classifier uses much fewer patterns than the JEP-Classifier [3] to achieve a similar (and, often, improved) accuracy, and 3) in many cases, it is superior to other state-of-the-art classification systems such as Naive Bayes, CBA, C4.5, and bagged and boosted versions of C4.5. We argue that SJEPs are high-quality patterns which possess the most differentiating power. As a consequence, they represent sufficient information for the construction of accurate classifiers. In addition, we generalize these patterns by introducing Noise-tolerant Emerging Patterns (NEPs) and Generalized Noise-tolerant Emerging Patterns (GNEPs). Our tree-based algorithms can be adopted to easily discover these variations. We experimentally demonstrate that SJEPs, NEPs, and GNEPs are extremely useful for building effective classifiers that can deal well with noise.