Improvement Of Frequent 1-Item Set Generation Method And Experimental Study

Since the ancient times,the collection and analysis of the date are in progress.For example,the ancients of the weather summary and prediction are in people's daily lives of weather changes,that is,the conclusions of the weather data collection and analysis;As well as crop cultivation time,planting methods are also based on the experience of people over the years to plant and summed up the way,which is also the data collection and analysis process;The rest of the construction,water conservancy,business,etc.,since ancient times,the collection of data and use reflected in all aspects of life.Before the advent of the Internet,people use the data are mostly confined to a regional context,the region's weather,regional crops and climate-adapting architectural style.With the emergence and development of the Internet,along with the formation of the world's information integration,people can more easily get more useful data,which means that more valuable things will be obtained from the data,which is now of data mining.Data mining is intended to find the value of data,mainly cluster analysis,classification analysis,correlation analysis,prediction and deviation analysis.The association analysis is the summary of the relevant items in the data,so as to carry out other analytical work,and also the direction associated with this article.In order to facilitate the analysis of association rules,a lot of association analysis algorithm appeared,which is to find the data items with strong correlation.In most association rule algorithms,frequent 1-itemsets need to be generated,and subsequent work will be continued on the basis of frequent 1-itemsets.For the analysis of association rules that need to be done only once,generation of frequent 1-itemsets need to be scanned once,but when the data is continually increasing and the association analysis is going on,every association analysis should be done frequently.Which means that the subsequent analysis of the need for re-association of the old data scanning,which will waste a lot of time.In this paper,the generation of frequent 1-itemsets is improved to save unnecessary scan time of database.The improvement of frequent 1-itemsets generation in the case of incremental data is mainly achieved by data dumping of candidate 1-itemsets in the process of generating frequent 1-itemsets.The principle is that the number of data items is greater than the number of data items,thus saving the time of generating frequent 1-itemsets in subsequent association rule analysis,thus saving the working time of the whole algorithm.