ACM Computing Surveys (CSUR)
Building Data Mining Applications for CRM
Building Data Mining Applications for CRM
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Construct robust rule sets for classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 3 - Volume 3
Discovering Knowledge in Data: An Introduction to Data Mining
Discovering Knowledge in Data: An Introduction to Data Mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Using association rules to make rule-based classifiers robust
ADC '05 Proceedings of the 16th Australasian database conference - Volume 39
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Applications quest: computing diversity
Communications of the ACM - Self managed systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A framework for simultaneous co-clustering and learning from complex data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining
ACNB: Associative Classification Mining Based on Naïve Bayesian Method
International Journal of Information Technology and Web Engineering
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Predictive models, such as rule based classifiers, often have difficulty with incomplete data e.g., erroneous/missing values. So, this work presents a technique used to reduce the severity of the effects of missing data on the performance of rule base classifiers using divisive data clustering. The Clustering Rule based Approach CRA clusters the original training data and builds a separate rule based model on the cluster wise data. The individual models are combined into a larger model and evaluated against test data. The effects of the missing attribute information for ordered and unordered rule sets is evaluated and the collective model CRA is experimentally used to show that its performance is less affected than the traditional model when the test data has missing attribute values, thus making it more resilient and robust to missing data.