Power-law distribution in encoded MFCC frames of speech, music, and environmental sound signals
Proceedings of the 21st international conference companion on World Wide Web
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Aggregating individual models of decision-making processes
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
Local implicit feedback mining for music recommendation
Proceedings of the sixth ACM conference on Recommender systems
Sentimental Spidering: Leveraging Opinion Information in Focused Crawlers
ACM Transactions on Information Systems (TOIS)
Maguro, a system for indexing and searching over very large text collections
Proceedings of the sixth ACM international conference on Web search and data mining
SWSNL: Semantic Web Search Using Natural Language
Expert Systems with Applications: An International Journal
Webzeitgeist: design mining the web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Extracting domain-specific opinion words for sentiment analysis
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Unicorn: a system for searching the social graph
Proceedings of the VLDB Endowment
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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.