Rank-Based Weighted Rule Mining Using Post Mining Methods with Ontology Support

摘要

Association rule mining is used to extract frequent item sets. Apriori algorithm is used to mine association rules. Minimum support and confidence values are used to identify interested rules. Low support threshold produces more number of rules. The rules are used for the decision making process. Rule reduction is required for efficient decision-making system. Knowledge based rule reduction schemes are used to filter the interested rules. Post mining schemes are used to filter derived rules. Pruning, summarizing, grouping, and visualization techniques are used for the post mining process. Uninterest or redundant rules are removed in pruning process. Concise sets of rules are generated in summarizing method. Grouping process produces groups of rules. Visualization produces graphical format of rules. Association Rule Interactive post-Processing using Schemas and Ontologies (ARIPSO) mechanism is used for post mining process. ARIPSO is used to prune and filter discovered rules. In the existing system rule validation is not provided. Quantitative attributes are not considered in the post-mining scheme. Weighted rule mining scheme is not supported. The proposed system is designed to perform post mining on derived rules with ontology support. The rule-mining scheme is enhanced to handle quantitative attributes. ARIPSO scheme is enhanced with validation methods. Weighted rule mining and filtering process can be integrated with the ARIPSO scheme. Rank based concept relationship analysis can be provided to improve the post mining process.