Efficient approach for interactively mining web traversal patterns

Web mining is one of the mining technologies, which applies data mining technique in large amount of web data to improve the web services. Web traversal pattern mining discovers most of users' access patterns from web logs. When we understand the users' behaviors, we can make some appropriate actions for different purposes. However, it is considerably difficult to select a perfect minimum support threshold during the mining procedure to find the interesting rules. Even though the experienced experts, they also cannot determine the appropriate minimum support to find the interesting rules. Thus, we must constantly adjust the minimum support until the satisfactory mining results can be found. This will waste a lot of time on these repeating mining processes with the same data. Therefore, many researchers pay attention to the interactive data mining in recent years. The essence of interactive data mining is that we can use the previous mining results to reduce the unnecessary processes when the minimum support is changed. In this paper, we propose an efficient interactive web traversal pattern mining algorithm to reduce the mining time and make the mining results to satisfy the users' requirements.