Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)

Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)

Bing Liu

Language: English

Pages: 624

ISBN: 3642268919

Format: PDF / Kindle (mobi) / ePub


Web mining aims to discover useful information and knowledge from Web hyperlinks, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semi-structured and unstructured nature of the Web data. The field has also developed many of its own algorithms and techniques.

Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. 

The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.

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simply change each value to an attribute–value pair. Example 6: The table data in Fig. 2.5(A) can be converted to the transaction data in Fig. 2.5(B). Each attribute–value pair is considered an item. Using only values is not sufficient in the transaction form because different attributes may have the same values. For example, without including attribute names, value a’s for Attribute1 and Attribute2 are not distinguishable. After the conversion, Fig. 2.5(B) can be used in mining. If an attribute

simply change each value to an attribute–value pair. Example 6: The table data in Fig. 2.5(A) can be converted to the transaction data in Fig. 2.5(B). Each attribute–value pair is considered an item. Using only values is not sufficient in the transaction form because different attributes may have the same values. For example, without including attribute names, value a’s for Attribute1 and Attribute2 are not distinguishable. After the conversion, Fig. 2.5(B) can be used in mining. If an attribute

3.3.1. growRule() function: growRule() generates a rule (called BestRule) by repeatedly adding a condition to its condition set that maximizes an evaluation function until the rule covers only some positive examples in GrowPos but no negative examples in GrowNeg. This is basically the same as lines 4–17 in Fig. 3.13, but without beam search (i.e., only the best rule is kept in each iteration). Let the current partially developed rule be R: R: av1, .., avk class where each avj is a condition

and knowledge management (CIKM-2002), 2002. Basu, S., A. Banerjee, and R. Mooney. Semi-supervised clustering by seeding. In Proceedings of International Conference on Machine Learning (ICML-2002), 2002. Bezdek, J.C. Cluster Validity with Fuzzy Sets. Journal of Cybernetics, 1974, 3: p. 58–72. Bradley, P., U. Fayyad, and C. Reina. Scaling clustering algorithms to large databases. Knowledge Discovery and Data Mining, 1998: p. 9–15. Cheeseman, P. and J. Stutz. Bayesian classification (AutoClass):

learning, the labeled set is small, but the unlabeled set is very large. So the EM’s parameter estimation is almost completely determined by the unlabeled set after the first iteration. This means that EM essentially performs unsupervised clustering. When the two mixture model assumptions are true, the natural clusters of the data are in correspondence with the class labels. The resulting clusters can be used as the classifier. However, when the assumptions are not true, the clustering can go

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