Clickstream Prediction Using Sequential Stream Mining Techniques with Markov Chains
Shelby D. Bernhard, Carson Kai-Sang Leung, Vanessa J. Reimer, Joshua Westlake · 2016
As one of data mining tasks, sequential pattern mining provides valuable information about frequent patterns of users over time. For instance, frequent sequential patterns can be applicable to analyze user clickstreams for determination of web navigation patterns, genome sequences, and customer purchasing patterns. In many real-life situations, data to be mined are continuously changing. Moreover, these data are streaming at a high velocity, which leads to impracticality of storing all these data in memory. Hence, to handle these situations, we propose three stream mining algorithms to first find frequent sequential patterns. The algorithms then form statistical models, which are stored as Markov chains or transition matrices capturing frequent sequential patterns mined so far, to predict future user clickstream (e.g., the web page the user will visit next). Experimental results show the efficiency and prediction accuracy of our proposed Markov chain-based sequential stream mining algorithms in clickstream prediction.