Multi-Instance Learning Based Web Mining.

In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. In this paper, a web mining problem, i.e. web index recommendation, is investigated from a multi-instance view. In detail, each web index page is regarded as a bag, while each of its linked pages is regarded as an instance. A user favoring an index page means that he or she is interested in at least one page linked by the index. Based on the browsing history of the user, recommendation could be provided for unseen index pages. An algorithm named Fretcit-κNN, which employs the Minimal Hausdorff distance between frequent term sets and utilizes both the references and citers of an unseen bug in determining its label, is proposed to solve the problem. Experiments show that in average the recommendation accuracy of Fretcit-κNN is 81.0% with 71.7% recall and 70.9% precision, which is significantly better than the best algorithm that does not consider the specific characteristics of multi-instance learning, whose performance is 76.3% accuracy with 63.4% recall and 66.1% precision. [ABSTRACT FROM AUTHOR]