Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
A new version of the rule induction system LERS
Fundamenta Informaticae
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
Database Systems: The Complete Book
Database Systems: The Complete Book
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
RainForest—A Framework for Fast Decision Tree Construction of Large Datasets
Data Mining and Knowledge Discovery
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
IEEE Transactions on Knowledge and Data Engineering
The effect of threshold values on association rule based classification accuracy
Data & Knowledge Engineering
A new approach to classification based on association rule mining
Decision Support Systems
Data & Knowledge Engineering
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Inducing oblique decision trees with evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Autonomous decision-making: a data mining approach
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Self organization of a massive document collection
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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This paper presents a new associative classification algorithm for data mining. The algorithm uses elementary set concepts, information entropy and database manipulation techniques to develop useful relationships between input and output attributes of large databases. These relationships (knowledge) are represented using IF-THEN association rules, where the IF portion of the rule includes a set of input attributes features and THEN portion of the rule includes a set of output attributes that represent decision outcome. Application of the algorithm is presented with a thermal spray process control case study. Thermal spray is a process of forming a desired shape of material by spraying melted metal on a ceramic mould. The goal of the study is to identify spray process input parameters that can be used to effectively control the process with the purpose of obtaining better characteristics for the sprayed material. Detailed discussion on the source and characteristics of the data sets is also presented.