Association Rules Algorithm Research And Scientific Data Mining Applications

Data Mining, also known as Knowledge Discovery in Database, distills knowledge from a mass of data. It is a new research area involving several branches of machine learning and containing many domains. Association rule is one of the most important domains among those of data mining, which finds interesting relationships between items or attributes from database. Association rule extraction from large database has become an active field.In the beginning of this thesis some basic principal theories, approaches and problems of data mining are introduced, followed by conceptions, categories and general thoughts of popular algorithms about association rule. A few classic association rule extracting algorithms are deeply discussed.In order to resolve frequent pattern mining problem efficiently, redundant operation and temporary data in FP-growth algorithm are analyzed, data structures, FPR-Tree and FPR-List, are imported, the method of conditional pattern base generation and storage in FP-growth algorithm is improved, and a new FPRSG algorithm is presented. Theoretical analysis and experiment result both show that FPRSG is more efficient than FP-growth.The combination of data mining and scientific research is a relatively new subject, and it is worth researching in many aspects. Large-scale scientific data have its unique characteristics, such as huge data quantities and complicated features, which usually makes it difficult to understand, analyze and extract knowledge from them. Thus, scientific data mining is imperative under the situation.In order to improve efficiency of scientific data storage and transportation, scientific data compression rate prediction method based on genetic algorithm is proposed, and association rule is used to evaluate its training result to find out whether the method is applicable to certain given scientific data. The evaluation results also have certain value to domain scientists.In the last part of the thesis, the conclusion and prospect of data mining research and application is given.