Research And Application Of Mining Access Sequential Pattern In Weblog

Data Mining is devoted to digital analysis and understanding, finding potential knowledge in the data. So in today,a large number of useful potential information can be taken out from the data which has been explosively growing. In recent years, Web applications are active in all aspects of social life, and the World Wide Web has become the world's largest information center. But a lot of useful information has been engulfed in massive data. It is urgent for us to apply data mining technology on web data analyzing. So web mining technology was born,and has become one of the most important applications of data mining.Based on the interesting web data,web mining is generally divided into three categories: Web content mining,Web structure mining and Web usage mining..Main work in this paper is to study web log mining,which is the branch of Web Usage Mining. Although it is feasible for general sequential pattern mining algorithms to mining sequential patterns from web server log files,but after pretreatment,the web log sequence database is different in sequence structure length from the general sequence pattern database. Therefore,to meet the unique and improve the efficiency of data mining,based on general algorithm,the web log sequential pattern mining algorithms need to be improved and enhanced.At present,the main challenge of mining access sequential pattern form web log is the high processing cost due to the large amount of data. In this paper,by combining the relatively high effieiency algorithms SPAM and Prefixspan,we propose a new algorithm Spam_Prefix_Weblog. It is efficiency of mining in support counting and candidate sequence generation is achieved with many techniques.We are sure that the new algorithm Spam_Prefix_Weblog can help us resolve many questions.