In this paper, the methods are investigate for online，frequent paRem mining of stream data,with the following contributions：(1) based on heuristic methodology and sample theory,step-by-step data stream mining method is used to estimate potential paRern set；(2)will find any length paRern not only single item pattern；(3)to find more appropriate length of each segment satisfying accuracy requirement,Hoeffding bound theory was introduced and revised to make it more suit for pattern mining；(4)a maintenance approach for estimating frequent patterns is developed for on．1ine analysis．Based on this design，estimation and maintenance algorithms are proposed for efficient analysis of data streams．This performance study compares the proposed algorithms and identifies the most accuracy-，memory-and time-efficient algorithms for stream data analysis．