Knowledge extraction is a process of filtering some informative knowledge from the database so that it can be used wide variety of applications and analysis. Due to this highly efficient algorithm is required for data mining and for accessing data from large datasets. In frequent item sets are produced from very big or huge data sets by applying some rules or association rule mining algorithms like Apriori technique, Partition method, Pincer-Search, Incremental, Border algorithm and many more, which take larger computing time to calculate all the frequent itemsets. As the network traffic increases we need an efficient system to monitor packet analysis of network flow data. Due to this frequent itemsets mining is basic problem in field of data mining and knowledge discovery. Here in this paper a brief survey of all the techniques related to frequent item sets generation has been given.