Application Of Improved Algorithms Of Association Rules To Recommendation System

Data mining is a promising and flourishing frontier in database systems and new database applications.Data mining,also popularly referred to as konwledge discovery in database(KDD),is the automated or convenient extraction of patterns representing knowledge.association rules mining is one of key filed of Data mining. Association rules mining finds interesting association or correlation relationships among a large set of data items.The discovery of interesting association rules can help the manager make buiness decision, such as atalog design, cross-marketing, and buying analysis.This paper based on the teaching platform of embedded online intelligence.It improved the association rules Algorithm and put it into the recommendation system. The author made the two systems into a whole.This article reads as follows:1: the paper illustrate source,classification,processing and method of Data Mining theory. The paper give some example about profit due to using the data mining technique.therefore,it is obvious that this issue has a great researching meaning.2: association rule mining algorithm concept,classification,procedure and other related knowledges, focusing on the Apriori mining algorithm and analysis shortage of mining algorithm, this research can prepare for future improvement.3: detailed analysis of the advanced Apriori algorithm, pointed out the shortcoming of this improvment. Basing on this, a new VipApriori algorithm come into being, the theoretical analysis and tests proved that the improved algorithm to achieve satisfactory results.4: put forward the individuation recommendation system.Details on Embedded Intelligent online teaching platform used by the building and the key techniques to enhance their websites smart, reuse, and robustness.5:VipApriori described in detail in the intelligent online platform of teaching - personalized intelligent learning system integration applications, and achieved satisfactory "personalized smart" teaching effect.The final summing up the results of the Institute, and proposed to be improved with insufficient part for the future to continue learning.