C4.5: programs for machine learning
C4.5: programs for machine learning
ECML '93 Proceedings of the European Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Mining top-K covering rule groups for gene expression data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
On Mining Instance-Centric Classification Rules
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Frequent Patterns from Uncertain Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
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
Direct Discriminative Pattern Mining for Effective Classification
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A Rule-Based Classification Algorithm for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Decision Trees for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Frequent pattern mining with uncertain data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic frequent itemset mining in uncertain databases
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
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
A decremental approach for mining frequent itemsets from uncertain data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining frequent patterns from univariate uncertain data
Data & Knowledge Engineering
Efficient computation of measurements of correlated patterns in uncertain data
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Top-k interesting phrase mining in ad-hoc collections using sequence pattern indexing
Proceedings of the 15th International Conference on Extending Database Technology
Learning very fast decision tree from uncertain data streams with positive and unlabeled samples
Information Sciences: an International Journal
An associative classifier for uncertain datasets
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Classification is one of the most essential tasks in data mining. Unlike other methods, associative classification tries to find all the frequent patterns existing in the input categorical data satisfying a user-specified minimum support and/or other discrimination measures like minimum confidence or information-gain. Those patterns are used later either as rules for rule-based classifier or training features for support vector machine (SVM) classifier, after a feature selection procedure which usually tries to cover as many as the input instances with the most discriminative patterns in various manners. Several algorithms have also been proposed to mine the most discriminative patterns directly without costly feature selection. Previous empirical results show that associative classification could provide better classification accuracy over many datasets. Recently, many studies have been conducted on uncertain data, where fields of uncertain attributes no longer have certain values. Instead probability distribution functions are adopted to represent the possible values and their corresponding probabilities. The uncertainty is usually caused by noise, measurement limits, or other possible factors. Several algorithms have been proposed to solve the classification problem on uncertain data recently, for example by extending traditional rule-based classifier and decision tree to work on uncertain data. In this paper, we propose a novel algorithm uHARMONY which mines discriminative patterns directly and effectively from uncertain data as classification features/rules, to help train either SVM or rule-based classifier. Since patterns are discovered directly from the input database, feature selection usually taking a great amount of time could be avoided completely. Effective method for computation of expected confidence of the mined patterns used as the measurement of discrimination is also proposed. Empirical results show that using SVM classifier our algorithm uHARMONY outperforms the state-of-the-art uncertain data classification algorithms significantly with 4% to 10% improvements on average in accuracy on 30 categorical datasets under varying uncertain degree and uncertain attribute number.