C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th 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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
MMAC: A New Multi-Class, Multi-Label Associative Classification Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
MCAR: multi-class classification based on association rule
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
ACN: An Associative Classifier with Negative Rules
CSE '08 Proceedings of the 2008 11th IEEE International Conference on Computational Science and Engineering
Text Categorization Based on Boosting Association Rules
ICSC '08 Proceedings of the 2008 IEEE International Conference on Semantic Computing
Associative Classifiers for Predictive Analytics: Comparative Performance Study
EMS '08 Proceedings of the 2008 Second UKSIM European Symposium on Computer Modeling and Simulation
ACCF: Associative Classification Based on Closed Frequent Itemsets
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Association Classification Based on Compactness of Rules
WKDD '09 Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining
2-PS based associative text classification
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
A Clustering Rule Based Approach for Classification Problems
International Journal of Data Warehousing and Mining
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Integrating association rule discovery and classification in data mining brings a new approach known as associative classification. Associative classification is a promising approach that often constructs more accurate classification models classifiers than the traditional classification approaches such as decision trees and rule induction. In this research, the authors investigate the use of associative classification on the high dimensional data in text categorization. This research focuses on prediction, a very important step in classification, and introduces a new prediction method called Associative Classification Mining based on Naïve Bayesian method. The running time is decreased by removing the ranking procedure that is usually the first step in ranking the derived Classification Association Rules. The prediction method is enhanced using the Naïve Bayesian Algorithm. The results of the experiments demonstrate high classification accuracy.