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• W2183257309 abstract "Request classification is one of important strategies of Web. User behavior analysis can make Web service more intelligent and secure. Recently, tree structures have become a popular way for storing and manipulating huge amount of data. The classification of these data can facilitate storage, retrieval, indexing, query answering and different processing operations. In this paper, we propose User- Classifier algorithm for rule based classification of tree structured data that to classify web log user. This algorithm is based on extracting special tree pattern from training dataset. Our experiments show that User- Classifier reduces running time. In the case of complete classification, User- Classifier shows the best classification quality. With the continuous growth of data in web, different knowledge discovery tasks have become increasingly important. In this paper we intend to focus on the classification task. This task can be defined as follow: Given a set of training data classified into some predefined categories, learn to automatically categorize new data. Classifying based on using only the content of trees ignores a significant amount of structural information hidden in the structure of trees. Therefore, recently (mainly from 2003) a growing interest has been emerged to develop new approaches which use the structural information of trees. (Zaki and Aggarwal, 2006) Discussed the idea of constructing structural rules and proposed XRule algorithm for classification of XML documents. During the training phase, this algorithm finds the structures which are most related to the class variable. During the testing phase these structures are used to perform the structural classification. (Zaki and Aggarwal, 2006) Showed that this classifier is significantly more effective than text classifiers due to its ability in use of distinguishing structures. For real datasets, the accuracy of XRule is 2- 4% better than CBA (Liu et al., 1998) and SVM (Joachims, 2002). Based on our best knowledge and to the time of writing this paper, XRule is the most effective algorithm for classification of tree structured data. In rule based classifiers (such as XRule), pattern extraction phase plays an important role on the accuracy and efficiency of the algorithm. Different types of tree patterns can be extracted from a forest of trees. In this paper, we improve XRule by using different types of patterns. We propose User- Classifier a structural classifier based on closed induced tree patterns. We show that in the complete classification User- Classifier gives the best classification quality compared to XRule. Furthermore, its running time and complexity are always less than XRule. Related Workes:" @default.
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• W2183257309 title "Proposing a Classification Algorithm for User Identification According To User Web Log Analysis" @default.
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