Web log data mining based on association rule

As the recent advent of the internet, most of the modern firms are now geared towards using the web technology in their daily as well as strategic activities. The web mining technology provides them with unprecedented ability to analyze web log data, which are seemingly full of useful information, but often lack of important and meaningful information. This indicates the need to develop an advanced inference mechanism extracting richer implication from the web mining results. In this sense, this paper presents a new algorithm of web mining, which is aimed at enriching the interpretation of the draft results of association rule mining. The proposed algorithm in this paper was built based on apriori algorithm proposed by Agrawal and Srikant, and contains the first support, the second support and Rel-confidence, and has been extensively used for the web mining recently. Finally, with an illustrative web log database, the obtained results prove the robustness of the proposed algorithm in this paper. However, there is still space for improvement. The future work should still consist of more experiments with other web log data sets to optimize the proposed algorithms.