Direct mining of discriminative and essential frequent patterns via model-based search tree
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining GPS traces and visual words for event classification
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Association Analysis Techniques for Bioinformatics Problems
BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
Correlated itemset mining in ROC space: a constraint programming approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
CP-summary: a concise representation for browsing frequent itemsets
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient itemset generator discovery over a stream sliding window
Proceedings of the 18th ACM conference on Information and knowledge management
Interestingness of Association Rules Using Symmetrical Tau and Logistic Regression
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
DisIClass: discriminative frequent pattern-based image classification
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Authorship classification: a syntactic tree mining approach
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
CIMDS: adapting postprocessing techniques of associative classification for malware detection
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Diagnosing memory leaks using graph mining on heap dumps
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Direct mining of discriminative patterns for classifying uncertain data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Constructing classification features using minimal predictive patterns
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Interval-orientation patterns in spatio-temporal databases
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
NDPMine: efficiently mining discriminative numerical features for pattern-based classification
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Interesting-phrase mining for ad-hoc text analytics
Proceedings of the VLDB Endowment
Scalable graph analyzing approach for software fault-localization
Proceedings of the 6th International Workshop on Automation of Software Test
Itemset mining: A constraint programming perspective
Artificial Intelligence
Authorship classification: a discriminative syntactic tree mining approach
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Direct local pattern sampling by efficient two-step random procedures
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Mining of Gap-Constrained Subsequences and Its Various Applications
ACM Transactions on Knowledge Discovery from Data (TKDD)
Efficiently finding the best parameter for the emerging pattern-based classifier PCL
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Classification based on specific rules and inexact coverage
Expert Systems with Applications: An International Journal
Top-k interesting phrase mining in ad-hoc collections using sequence pattern indexing
Proceedings of the 15th International Conference on Extending Database Technology
I-prune: Item selection for associative classification
International Journal of Intelligent Systems
Effective use of frequent itemset mining for image classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Efficient mining of top-k breaker emerging subgraph patterns from graph datasets
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
Mining succinct predicated bug signatures
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
Zips: mining compressing sequential patterns in streams
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics
Exploring discriminative pose sub-patterns for effective action classification
Proceedings of the 21st ACM international conference on Multimedia
Investigating clinical care pathways correlated with outcomes
BPM'13 Proceedings of the 11th international conference on Business Process Management
Troubleshooting interactive complexity bugs in wireless sensor networks using data mining techniques
ACM Transactions on Sensor Networks (TOSN)
CAR-NF: A classifier based on specific rules with high netconf
Intelligent Data Analysis
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The application of frequent patterns in classification has demonstrated its power in recent studies. It often adopts a two-step approach: frequent pattern (or classification rule) mining followed by feature selection (or rule ranking). However, this two-step process could be computationally expensive, especially when the problem scale is large or the minimum support is low. It was observed that frequent pattern mining usually produces a huge number of "patterns" that could not only slow down the mining process but also make feature selection hard to complete. In this paper, we propose a direct discriminative pattern mining approach, DDPMine, to tackle the efficiency issue arising from the two-step approach. DDPMine performs a branch-and-bound search for directly mining discriminative patterns without generating the complete pattern set. Instead of selecting best patterns in a batch, we introduce a "feature-centered" mining approach that generates discriminative patterns sequentially on a progressively shrinking FP-tree by incrementally eliminating training instances. The instance elimination effectively reduces the problem size iteratively and expedites the mining process. Empirical results show that DDPMine achieves orders of magnitude speedup without any downgrade of classification accuracy. It outperforms the state-of-the-art associative classification methods in terms of both accuracy and efficiency.